@article{basnet_brill_ranjithan_mahinthakumar_2023, title={Supervised Machine Learning Approaches for Leak Localization in Water Distribution Systems: Impact of Complexities of Leak Characteristics}, volume={149}, ISSN={["1943-5452"]}, DOI={10.1061/JWRMD5.WRENG-6047}, abstractNote={Localizing pipe leaks is a significant challenge for water utilities worldwide. Pipe leaks in water distribution systems (WDSs) can cause the loss of a large amount of treated water, leading to pressure loss, increased energy costs, and contamination risks. What makes localizing pipe leaks challenging is the underground location of the water pipes and the similarity in impact on hydraulic properties (e.g., pressure, flow) due to leaks as compared to the effects of WDS operational changes. Physical methods to locate leaks are expensive, intrusive, and heavily localized. Computational approaches such as data-driven machine learning models provide an economical alternative to physical methods. Machine learning models are readily available and easily customizable to most problems; therefore, there is an increasing trend in their application for leak localization in WDSs. While several studies have applied machine learning models to localize leaks in single pipes and small test networks, these studies have yet to thoroughly test these models against the different complexities of leak localization problems, and hence their applicability to real-world WDSs is still unclear. The simplicity of the WDSs, the oversimplification of leak characteristics, and the lack of consideration of modeling and measuring device uncertainties adopted in most of these studies make the scalability of their proposed approaches questionable to real-world WDSs. Our study addresses this issue by devising four study cases of different complexity that account for realistic leak characteristics and model- and measuring device-related uncertainties. Two established machine learning models—multilayer perceptron (MLP) and convolutional neural network (CNN)—are trained and tested for their ability to localize the leaks and predict their sizes for each of the four study cases using different simulated hydraulic inputs. In addition, the potential benefit of combining different types of hydraulic data as inputs to the machine learning models in localizing leaks is also explored. Pressure and flow, two common hydraulic measurements, are used as inputs to the machine learning models. Further, the impact of single and multiple time point input in leak localization is also investigated. The results for the L-Town network indicate good accuracies for both the models for all study cases, with CNN consistently outperforming MLP.}, number={8}, journal={JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT}, author={Basnet, Lochan and Brill, Downey and Ranjithan, Ranji and Mahinthakumar, Kumar}, year={2023}, month={Aug} }
 @article{jaunich_levis_decarolis_barlaz_ranjithan_2020, title={Exploring alternative solid waste management strategies for achieving policy goals}, volume={53}, ISSN={0305-215X 1029-0273}, url={http://dx.doi.org/10.1080/0305215X.2020.1759578}, DOI={10.1080/0305215X.2020.1759578}, abstractNote={The authors previously analysed a real-world solid waste management (SWM) system using the solid waste optimization life-cycle framework (SWOLF) to identify optimal SWM strategies that meet modelled objectives (e.g. cost, environmental impacts, landfill diversion). While mathematically optimal strategies can support SWM decision making, they may not be readily implementable because of unmodelled objectives (e.g. practical limitations, social preferences, political and management considerations). A mathematical programming technique extending SWOLF is used to systematically identify, for several scenarios, different ‘optimal’ SWM strategies that are maximally different from each other in terms of waste flows, while meeting modelled objectives and constraints. The performance with respect to unmodelled issues was analysed to demonstrate the flexibility in potential strategies. Practitioner feedback highlighted implementation challenges due to existing practices; however, insights gained from this exercise led to more plausible and acceptable strategies by incrementally modifying the initial SWM alternatives generated.}, number={5}, journal={Engineering Optimization}, publisher={Informa UK Limited}, author={Jaunich, Megan K. and Levis, James W. and DeCarolis, Joseph F. and Barlaz, Morton A. and Ranjithan, S. Ranji}, year={2020}, month={Jun}, pages={1–14} }
 @article{rossi_oliveira favretto_grassi_decarolis_cho_hill_soares chvatal_ranjithan_2019, title={Metamodels to assess the thermal performance of naturally ventilated, low-cost houses in Brazil}, volume={204}, ISSN={["1872-6178"]}, DOI={10.1016/j.enbuild.2019.109457}, abstractNote={Building performance simulation [BPS] tools are important in all design stages. However, barriers such as time, resources, and expertise inhibit their use in the early design stages. This study aims to develop, as part of decision-support framework, metamodels to assess the thermal discomfort in a naturally ventilated Brazilian low-cost house during early design. The metamodels predict the degree-hours of discomfort by heat and/or by cold as a function of design parameters for three Brazilian cities: Curitiba, São Paulo, and Manaus. The key design parameters, related with passive design strategies, are building orientation, shading devices position and dimensions, thermal properties of the walls and roof, window-to-wall ratio, and effective window ventilation area. The method consists of three main stages: (i) baseline model development; (ii) Monte Carlo simulation; (iii) multivariate regression. Overall, the metamodels showed R2 values higher than 0.95 for all climates, except the ones predicting discomfort by heat for Curitiba (R2 =0.61) and São Paulo (R2 =0.75). The proposed metamodels can quickly and accurately assess the thermal performance of naturally ventilated low-cost houses. They can be used to guide professionals during the early design stages, and for educational purposes in building design pedagogy.}, journal={ENERGY AND BUILDINGS}, author={Rossi, Michele Marta and Oliveira Favretto, Ana Paula and Grassi, Camila and DeCarolis, Joseph and Cho, Soolyeon and Hill, David and Soares Chvatal, Karin Maria and Ranjithan, Ranji}, year={2019}, month={Dec} }
 @article{jaunich_levis_decarolis_barlaz_ranjithan_2019, title={Solid Waste Management Policy Implications on Waste Process Choices and Systemwide Cost and Greenhouse Gas Performance}, volume={53}, ISSN={0013-936X 1520-5851}, url={http://dx.doi.org/10.1021/acs.est.8b04589}, DOI={10.1021/acs.est.8b04589}, abstractNote={Solid waste management (SWM) is a key function of local government and is critical to protecting human health and the environment. Development of effective SWM strategies should consider comprehensive SWM process choices and policy implications on system-level cost and environmental performance. This analysis evaluated cost and select environmental implications of SWM policies for Wake County, North Carolina using a life-cycle approach. A county-specific data set and scenarios were developed to evaluate alternatives for residential municipal SWM, which included combinations of a mixed waste material recovery facility (MRF), anaerobic digestion, and waste-to-energy combustion in addition to existing SWM infrastructure (composting, landfilling, single stream recycling). Multiple landfill diversion and budget levels were considered for each scenario. At maximum diversion, the greenhouse gas (GHG) mitigation costs ranged from 30 to 900 $/MTCO2e; the lower values were when a mixed waste MRF was used, and the higher values when anaerobic digestion was used. Utilization of the mixed waste MRF was sensitive to the efficiency of material separation and operating cost. Maintaining the current separate collection scheme limited the potential for cost and GHG reductions. Municipalities seeking to cost-effectively increase landfill diversion while reducing GHGs should consider waste-to-energy, mixed waste separation, and changes to collection.}, number={4}, journal={Environmental Science & Technology}, publisher={American Chemical Society (ACS)}, author={Jaunich, Megan K. and Levis, James W. and DeCarolis, Joseph F. and Barlaz, Morton A. and Ranjithan, S. Ranji}, year={2019}, month={Jan}, pages={1766–1775} }
 @article{salomons_skulovich_ostfeld_2017, title={Battle of Water Networks DMAs: Multistage Design Approach}, volume={143}, ISSN={["1943-5452"]}, DOI={10.1061/(asce)wr.1943-5452.0000830}, abstractNote={AbstractLooped water distribution system (WDS) repartitioning to district metering areas (DMAs) gained popularity as an effective technique to manage the system and detect and reduce system leakage...}, number={10}, journal={JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT}, author={Salomons, Elad and Skulovich, Olya and Ostfeld, Avi}, year={2017}, month={Oct} }
 @article{karam_mcmillan_lai_reyes_sederoff_grunden_ranjithan_levis_ducoste_2017, title={Construction and setup of a bench-scale algal photosynthetic bioreactor with temperature, light, and ph monitoring for kinetic growth tests}, number={124}, journal={Jove-Journal of Visualized Experiments}, author={Karam, A. L. and McMillan, C. C. and Lai, Y. C. and Reyes, F. L. and Sederoff, H. W. and Grunden, A. M. and Ranjithan, R. S. and Levis, J. W. and Ducoste, J. J.}, year={2017} }
 @article{al gharably_decarolis_ranjithan_2016, title={An enhanced linear regression-based building energy model (LRBEM plus ) for early design}, volume={9}, ISSN={["1940-1507"]}, DOI={10.1080/19401493.2015.1004108}, abstractNote={The design community lacks simple, data-driven energy assessment tools to explore energy-efficient alternatives during the early stages of building design. A promising option is to utilize a whole building energy simulation engine (e.g. EnergyPlus) within a Monte Carlo simulation framework to develop a linear regression-based building energy model (LRBEM) that can predict idealized heating and cooling loads based on parameters relevant to early design. Previous work was limited to medium-sized US commercial office buildings with rectangular geometries. A key limitation is addressed in this paper by considering complex geometries. A reformulated model, LRBEM+, is developed and tested with a suite of building geometries that represent limiting cases. The resultant relative error between LRBEM+ and EnergyPlus is generally less than 10%. Furthermore, LRBEM+ correctly predicts the direction and magnitude of changes in heating and cooling loads in response to changes in the most influential early design parameters.}, number={2}, journal={JOURNAL OF BUILDING PERFORMANCE SIMULATION}, author={Al Gharably, Maged and DeCarolis, Joseph F. and Ranjithan, S. Ranji}, year={2016}, month={Mar}, pages={115–133} }
 @article{wang_brill_ranjithan_sankarasubramanian_2015, title={A framework for incorporating ecological releases in single reservoir operation}, volume={78}, ISSN={0309-1708}, url={http://dx.doi.org/10.1016/j.advwatres.2015.01.006}, DOI={10.1016/j.advwatres.2015.01.006}, abstractNote={Most reservoir operation practices consider downstream environmental flow as a constraint to meet a minimum release. The resulting flow regime may not necessarily provide downstream aquatic conditions to support healthy ecosystems. These effects can be quantified in terms of changes in values of parameters that represent the flow regimes. Numerous studies have focused on determining the ecological response to hydrological alteration caused by reservoir operation. To mitigate hydrological alteration and restore the natural flow regime as much as possible, a reservoir operation framework is proposed to explicitly incorporate ecological flow requirements. A general optimization-based decision model is presented to consider simultaneously the multiple anthropogenic uses of the reservoir and desirable ecological releases represented by parameters that capture the flow regime. Multiple uses of the reservoir, including water supply, hydropower generation, etc., are modeled as a mixed integer programming problem. Hydropower generation, which is represented by a nonlinear function that usually depends on head and water flow, is linearized using a two-dimensional function. Investigations using a reservoir in Virginia, located in the southeastern United States, demonstrate that compared to standard releases based on current operation practice, releases simulated using this framework perform better in mimicking pre-development flows. The tradeoff between anthropogenic use and ecological releases is investigated. The framework is first demonstrated for instances with perfect stream flow information. To examine the flexibility of this framework in reservoir release management, monthly flow forecasts and disaggregated daily flow conditions are incorporated. Retrospective monthly flow forecasts are obtained through regression models that use gridded precipitation forecasts and gridded soil moisture estimates as predictors. A nonparametric method is chosen to disaggregate monthly flow forecasts to daily flow conditions. Compared with daily flow climatology, forecasted monthly and daily flow better preserves flow variability and result in lower changes of flow parameters under the proposed framework.}, journal={Advances in Water Resources}, publisher={Elsevier BV}, author={Wang, Hui and Brill, Earl D. and Ranjithan, Ranji S. and Sankarasubramanian, A.}, year={2015}, month={Apr}, pages={9–21} }
 @article{li_sankarasubramanian_ranjithan_sinha_2015, title={Role of multimodel combination and data assimilation in improving streamflow prediction over multiple time scales}, volume={30}, ISSN={1436-3240 1436-3259}, url={http://dx.doi.org/10.1007/s00477-015-1158-6}, DOI={10.1007/s00477-015-1158-6}, number={8}, journal={Stochastic Environmental Research and Risk Assessment}, publisher={Springer Science and Business Media LLC}, author={Li, Weihua and Sankarasubramanian, A. and Ranjithan, R. S. and Sinha, Tushar}, year={2015}, month={Sep}, pages={2255–2269} }
 @article{wang_sankarasubramanian_ranjithan_2015, title={Understanding the low-frequency variability in hydroclimatic attributes over the southeastern US}, volume={521}, ISSN={["1879-2707"]}, DOI={10.1016/j.jhydrol.2014.09.081}, abstractNote={Most studies on evaluating the potential in developing seasonal to interannual hydroclimatic forecasts have focused on associating low-frequency climatic conditions with basin-level precipitation/streamflow. The motivation of this study is to provide an understanding on how land surface characteristics modulate the low-frequency (interannual to decadal) variability in precipitation to develop low-frequency signal in streamflow. For this purpose, we consider basins with minimum anthropogenic impacts over southeastern United States and apply Singular Spectrum Analysis (SSA), a data-driven spectrum analysis tool, on annual precipitation and streamflow time series for detecting the dominant frequencies and for estimating the associated variability with them. Hypothesis test against an AR(1) process is carried out via Monte Carlo SSA for detecting significant (at 90% confidence level) low-frequency oscillations. Thus, the study investigates how the observed low-frequency oscillations in precipitation/streamflow vary over the southeastern United States and also their associations with climatic conditions. For most study basins, precipitation exhibits higher low-frequency oscillations than that of streamflow primarily due to reduction in variability by basin storage. Investigating this further, we found that the percentage variance accounted by low-frequency oscillations in streamflow being higher for larger basins which primarily indicates the increased role of climate and basin storage. To develop a fundamental understanding on how basin storage controls the low-frequency oscillations in streamflow, a simple annual hydrological model is employed to explore how the given low-frequency signal in precipitation being modified under different baseflow index conditions and groundwater residence time. Implications of these analyses relating to streamflow predictions and model calibration are also discussed.}, journal={JOURNAL OF HYDROLOGY}, author={Wang, Hui and Sankarasubramanian, A. and Ranjithan, R. S.}, year={2015}, month={Feb}, pages={170–181} }
 @article{jin_ranjithan_mahinthakumar_2014, title={A Monitoring Network Design Procedure for Three-Dimensional (3D) Groundwater Contaminant Source Identification}, volume={15}, ISSN={["1527-5930"]}, DOI={10.1080/15275922.2013.873095}, abstractNote={Finding the location and concentration of contaminant sources is an important step in groundwater remediation and management. This discovery typically requires the solution of an inverse problem. This inverse problem can be formulated as an optimization problem where the objective function is the sum of the square of the errors between the observed and predicted values of contaminant concentration at the observation wells. Studies show that the source identification accuracy is dependent on the observation locations (i.e., network geometry) and frequency of sampling; thus, finding a set of optimal monitoring well locations is very important for characterizing the source. The objective of this study is to propose a sensitivity-based method for optimal placement of monitoring wells by incorporating two uncertainties: the source location and hydraulic conductivity. An optimality metric called D-optimality in combination with a distance metric, which tends to make monitoring locations as far apart from each other as possible, is developed for finding optimal monitoring well locations for source identification. To address uncertainty in hydraulic conductivity, an integration method of multiple well designs is proposed based on multiple hydraulic conductivity realizations. Genetic algorithm is used as a search technique for this discrete combinatorial optimization problem. This procedure was applied to a hypothetical problem based on the well-known Borden Site data in Canada. The results show that the criterion-based selection proposed in this paper provides improved source identification performance when compared to uniformly distributed placement of wells.}, number={1}, journal={ENVIRONMENTAL FORENSICS}, author={Jin, Xin and Ranjithan, Ranji S. and Mahinthakumar, G.}, year={2014}, month={Jan}, pages={78–96} }
 @article{marchi_salomons_ostfeld_kapelan_simpson_zecchin_maier_wu_elsayed_song_et al._2014, edition={+59 co-authors}, title={Battle of the Water Networks II}, volume={140}, ISSN={0733-9496 1943-5452}, url={http://dx.doi.org/10.1061/(ASCE)WR.1943-5452.0000378}, DOI={10.1061/(ASCE)WR.1943-5452.0000378}, abstractNote={The Battle of the Water Networks II (BWN-II) is the latest of a series of competitions related to the design and operation of water distribution systems (WDSs) undertaken within the Water Distribution Systems Analysis (WDSA) Symposium series. The BWN-II problem specification involved a broadly defined design and operation problem for an existing network that has to be upgraded for increased future demands, and the addition of a new development area. The design decisions involved addition of new and parallel pipes, storage, operational controls for pumps and valves, and sizing of backup power supply. Design criteria involved hydraulic, water quality, reliability, and environmental performance measures. Fourteen teams participated in the Battle and presented their results at the 14th Water Distribution Systems Analysis conference in Adelaide, Australia, September 2012. This paper summarizes the approaches used by the participants and the results they obtained. Given the complexity of the BWN-II problem and the innovative methods required to deal with the multiobjective, high dimensional and computationally demanding nature of the problem, this paper represents a snap-shot of state of the art methods for the design and operation of water distribution systems. A general finding of this paper is that there is benefit in using a combination of heuristic engineering experience and sophisticated optimization algorithms when tackling complex real-world water distribution system design problems. (C) 2014 American Society of Civil Engineers.}, number={7}, journal={Journal of Water Resources Planning and Management}, publisher={American Society of Civil Engineers (ASCE)}, author={Marchi, Angela and Salomons, Elad and Ostfeld, Avi and Kapelan, Zoran and Simpson, Angus R. and Zecchin, Aaron C. and Maier, Holger R. and Wu, Zheng Yi and Elsayed, Samir M. and Song, Yuan and et al.}, year={2014}, month={Jul}, pages={04014009} }
 @article{li_sankarasubramanian_ranjithan_brill_2014, title={Improved regional water management utilizing climate forecasts: An interbasin transfer model with a risk management framework}, volume={50}, ISSN={0043-1397}, url={http://dx.doi.org/10.1002/2013WR015248}, DOI={10.1002/2013wr015248}, abstractNote={Abstract}, number={8}, journal={Water Resources Research}, publisher={American Geophysical Union (AGU)}, author={Li, Weihua and Sankarasubramanian, A. and Ranjithan, R. S. and Brill, E. D.}, year={2014}, month={Aug}, pages={6810–6827} }
 @article{levis_barlaz_decarolis_ranjithan_2014, title={Systematic Exploration of Efficient Strategies to Manage Solid Waste in U.S. Municipalities: Perspectives from the Solid Waste Optimization Life-Cycle Framework (SWOLF)}, volume={48}, ISSN={0013-936X 1520-5851}, url={http://dx.doi.org/10.1021/es500052h}, DOI={10.1021/es500052h}, abstractNote={Solid waste management (SWM) systems must proactively adapt to changing policy requirements, waste composition, and an evolving energy system to sustainably manage future solid waste. This study represents the first application of an optimizable dynamic life-cycle assessment framework capable of considering these future changes. The framework was used to draw insights by analyzing the SWM system of a hypothetical suburban U.S. city of 100 000 people over 30 years while considering changes to population, waste generation, and energy mix and costs. The SWM system included 3 waste generation sectors, 30 types of waste materials, and 9 processes for waste separation, treatment, and disposal. A business-as-usual scenario (BAU) was compared to three optimization scenarios that (1) minimized cost (Min Cost), (2) maximized diversion (Max Diversion), and (3) minimized greenhouse gas (GHG) emissions (Min GHG) from the system. The Min Cost scenario saved $7.2 million (12%) and reduced GHG emissions (3%) relative to the BAU scenario. Compared to the Max Diversion scenario, the Min GHG scenario cost approximately 27% less and more than doubled the net reduction in GHG emissions. The results illustrate how the timed-deployment of technologies in response to changes in waste composition and the energy system results in more efficient SWM system performance compared to what is possible from static analyses.}, number={7}, journal={Environmental Science & Technology}, publisher={American Chemical Society (ACS)}, author={Levis, James W. and Barlaz, Morton A. and DeCarolis, Joseph F. and Ranjithan, S. Ranji}, year={2014}, month={Mar}, pages={3625–3631} }
 @inproceedings{jasper_mahinthakumar_ranjithan_brill_2013, title={A Sensitivity Analysis of Data Measurement Types for Leak Detection in Water Distribution Systems}, ISBN={9780784412947}, url={http://dx.doi.org/10.1061/9780784412947.059}, DOI={10.1061/9780784412947.059}, abstractNote={It is estimated that 15-40% of water is unaccounted for in urban water systems. This is mostly caused by small leaks, which are difficult to locate. Routinely measured pressure, flow, and water quality data can be used to locate leaks in the water network using an inverse modeling approach. For a known sensor configuration, the leak locations can be found by minimizing the difference between real and simulated measurements. However, when comparing measurement types (pressure, flow, or quality), some may be more sensitive to leak location than others. Furthermore, some measurement types may be more or less sensitive depending on the leak magnitude or the proximity of the leak to the sensors. The measurements types that are more sensitive to location will have a stronger signature and would need to be weighted more in an inverse modeling approach, especially in the presence of noise. Preliminary research suggests that water quality measurements are more sensitive to leak location when a leak is small, and that flow measurements are more sensitive when a leak is large. In this research, a series of sensitivity analyses are conducted on different networks to investigate the sensitivity of these measurements with respect to leak location, magnitude, and proximity of sensors to the leak location. 1. Motivation Water distribution systems are a vital part of modern infrastructure, yet they are susceptible to leaks and contaminant intrusion. High pressure, freezing water, or aging can cause cracks in the distribution pipes that lead to small, gradual leaks into the ground that are difficult to detect. In some aging systems, up to 40% of water is lost to leaks [1]. Utilities typically monitor locations that are prone to leak, based on a history of previous leaks or the age of the pipes. A leak can be detected, for example, by using acoustic listening devices that pick up on the sound of water escaping from the pipe, among other methods. However, it is expensive and time intensive to manually check the suspected pipes. There are routinely collected measurements of pressure, flow, and water quality at sensor locations. These measurements can carry a signature that will help identify the leak location and}, booktitle={World Environmental and Water Resources Congress 2013}, publisher={American Society of Civil Engineers}, author={Jasper, Micah N. and Mahinthakumar, Gnanamanikam (Kumar) and Ranjithan, Sanmugavadivel (Ranji) and Brill, Earl Downey}, year={2013}, month={May} }
 @article{levis_barlaz_decarolis_ranjithan_2013, title={A generalized multistage optimization modeling framework for life cycle assessment-based integrated solid waste management}, volume={50}, ISSN={1364-8152}, url={http://dx.doi.org/10.1016/j.envsoft.2013.08.007}, DOI={10.1016/j.envsoft.2013.08.007}, abstractNote={Solid waste management (SWM) is an integral component of civil infrastructure and the global economy, and is a growing concern due to increases in population, urbanization, and economic development. In 2011, 1.3 billion metric tons of municipal solid waste (MSW) were generated, and this is expected to grow to 2.2 billion metric tons by 2025. In the U.S., MSW systems processed approximately 250 million tons of waste and produced 118 Tg of CO2e emissions, which represents over 8% of non-energy related greenhouse gas (GHG) emissions, and 2% of total net GHG emissions. While previous research has applied environmental life cycle assessment (LCA) to SWM using formal search techniques, existing models are either not readily generalizable and scalable, or optimize only a single time period and do not consider changes likely to affect SWM over time, such as new policy and technology innovation. This paper presents the first life cycle-based framework to optimize—over multiple time stages—the collection and treatment of all waste materials from curb to final disposal by minimizing cost or environmental impacts while considering user-defined emissions and waste diversion constraints. In addition, the framework is designed to be responsive to future changes in energy and GHG prices. This framework considers the use of existing SWM infrastructure as well as the deployment and utilization of new infrastructure. Several scenarios, considering cost, diversion, and GHG emissions, are analyzed in a 3-stage test system. The results show the utility of the multi-stage framework and the insights that can be gained from using such a framework. The framework was also used to solve a larger SWM system; the results show that the framework solves in reasonable time using typical hardware and readily available mathematical programming solvers. The framework is intended to inform SWM by considering costs, environmental impacts, and policy constraints.}, journal={Environmental Modelling & Software}, publisher={Elsevier BV}, author={Levis, James W. and Barlaz, Morton A. and DeCarolis, Joseph F. and Ranjithan, S. Ranji}, year={2013}, month={Dec}, pages={51–65} }
 @article{jung_mahinthakumar_ranjithan_2013, title={Development of a simultaneous search-based pilot point method for subsurface characterization}, volume={27}, ISSN={["1436-3259"]}, DOI={10.1007/s00477-013-0734-x}, number={8}, journal={STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT}, author={Jung, Yong and Mahinthakumar, G. and Ranjithan, Ranji}, year={2013}, month={Dec}, pages={2003–2013} }
 @article{wang_sankarasubramanian_ranjithan_2013, title={Integration of Climate and Weather Information for Improving 15-Day-Ahead Accumulated Precipitation Forecasts}, volume={14}, ISSN={["1525-7541"]}, DOI={10.1175/jhm-d-11-0128.1}, abstractNote={Abstract}, number={1}, journal={JOURNAL OF HYDROMETEOROLOGY}, author={Wang, Hui and Sankarasubramanian, A. and Ranjithan, Ranji S.}, year={2013}, month={Feb}, pages={186–202} }
 @inproceedings{jasper_mahinthakumar_ranjithan_brill_2013, title={Leak Detection in Water Distribution Systems Using the Dividing Rectangles (DIRECT) Search}, ISBN={9780784412947}, url={http://dx.doi.org/10.1061/9780784412947.078}, DOI={10.1061/9780784412947.078}, abstractNote={Leak detection and management is an important problem in water distribution systems since it has been documented that up to 40% of the water may be lost to leaks in many aging systems. Small gradual leaks, which represent more than half of all leaks, are difficult to locate. Routinely measured pressure, flow, and water quality data in combination with a simulation-optimization inverse modeling approach could be used to characterize leakage. In this approach, the leak locations are found by minimizing the difference between real and simulated measurements for a known sensor configuration. Simulation-optimization approaches are computationally demanding because millions of simulations of a network simulator (e.g., EPANET) may be required to achieve a satisfactory solution. This problem is alleviated using a high performance computing (HPC) framework that enables many parallel simulations of the water system using EPANET. This research is modifying an existing global search algorithm, called the Dividing Rectangles (DIRECT) Search that is traditionally used for continuous functions, to enable parallel simulations and a mix of discrete variables (for leak locations) and continuous variables (for leak magnitudes). The modified algorithm is being tested with traditional continuous test functions, discrete test functions, and test water distribution networks. 1. Motivation Water distribution systems are a vital part of modern infrastructure, yet they are susceptible to leaks and contaminant intrusion. High pressure, freezing water, or aging can cause cracks in the distribution pipes that lead to small, gradual leaks into the ground that are difficult to detect. In some aging systems, up to 40% of water is lost to leaks [1]. Utilities typically monitor locations that are prone to leak, based on a history of previous leaks or the age of the pipes. A leak can be detected, for example, by using acoustic listening devices that pick up on the sound of water escaping from the pipe, among other methods. However, it is expensive and time intensive to manually check the suspected pipes. There are routinely collected measurements of pressure, flow, and water quality at sensor locations. These measurements can carry a signature that will help identify the leak location and}, booktitle={World Environmental and Water Resources Congress 2013}, publisher={American Society of Civil Engineers}, author={Jasper, Micah N. and Mahinthakumar, Gnanamanikam (Kumar) and Ranjithan, Sanmugavadivel (Ranji) and Brill, Earl Downey}, year={2013}, month={May} }
 @article{cai_vogel_ranjithan_2013, title={Special Issue on the Role of Systems Analysis in Watershed Management}, volume={139}, ISSN={["1943-5452"]}, DOI={10.1061/(asce)wr.1943-5452.0000341}, abstractNote={Watersheds are coupled human-natural systems (CHNSs) characterized by interactions between human activities and natural processes crossing a broad range of spatial and temporal scales. As stressed by a National Research Council (NRC) report (1999), watershed management poses an enormous challenge in the coming decades. The USDA and the EPA adopted a watershed approach to manage watersheds considering the interdependence among human, abiotic, and biotic components and the feedbacks that arise among management practices and their socioeconomic and environmental consequences. Concurrently, the attention of the environmental and water resources systems research community has evolved from the management of individual reservoirs, storm water, and aquifer systems to more integrated watershed or river basin systems. The application of systems analysis tools including simulation, optimization, and their integration offers an analytical mindset and a diversity of tools capable of addressing the complex challenges, which arise from human-natural interactions as well as communicating subsequent analyses to decision makers. Methods of systems analysis have been integral to water resources systems planning and management since the 1960s. Initially, methods of simulation, mathematical programming, and decision analysis borrowed from the field of operations research were applied to water management challenges. Later, in the 1990s, innovations in complex systems arising, in part, from previous contributions from catastrophe theory in the 1970s and chaos theory in the 1980s began to be applied to the field of water resources planning and management. Today, the application of all of these methods that are termed a systems approach remains critical to our field. Perhaps now more than ever before, systems methods are needed to solve watershed management problems due to the emergence of numerous new concerns relating to stakeholder participation, environmental ethics, life-cycle analysis, sustainability, industrial ecology, and design for ecological (as opposed to engineering) resilience (Dobson and Beck 1999). Both practitioners and researchers routinely face watershed management challenges, including, for example, restoring degraded ecosystems to achieve a balance between human and nature, resolving conflicts over protection of open space and environmental quality and development interests, and more generally accommodating within a watershed context water requirements for food, energy, and environment. Addressing these and other challenges requires the development of innovative systems concepts, methods, and algorithms for effective watershed management that can lead to both socioeconomic and environmental sustainability. Recent scientific, technological, and institutional developments have already and will continue to facilitate integrated watershed systems analysis approaches. We expect innovations relating to a wide range of emerging areas to continue facilitating development of watershed systems analysis including, but not limited to (1) distributed watershed hydrologic modeling and digital watersheds facilitated by hydro-informatics with improved forecast capacity; (2) increasing availability of distributed and digital datasets [e.g., remote sensing, sensor-based monitoring, and cyberinfrastructure (CI)]; (3) multidisciplinary research efforts among hydrologists, ecologists, economists, systems experts, and others; (4) institutional and financial support for watershed restoration practices; (5) improvements in computational and optimization algorithms; and (6) evolution in our ability to integrate ecological, environmental, and social objectives into what was once only a more narrow economic analysis (Lund and Cai 2006). Perhaps the most important developments of all relating to the application of water resources systems methods involve advances in computational sciences that have made possible more advanced quantitative analyses and have moved research more broadly into modeling of a watershed or a river basin as an integrated system of, e.g., reservoirs, aquifers, wetlands, and drainage systems. The goal of this special issue is to publish a representative set of papers focused on the field of watershed management modeling [see Zoltay et al. (2010)], which embraces and extends the myriad of recent advances described previously. This special issue is expected to serve the water resources management and planning community by highlighting the current state of some innovative research findings relating to applications of systems methods for solving various watershed management modeling problems. These problems include nonpoint source pollution management in urban or rural watersheds (papers by Jacobi et al., McGarity, Woodbury and Shoemaker, and Limbrunner et al.), water supply (paper by Giacomoni et al.), water allocation (papers by Riegels et al. and Pulido-Velazquez et al.), flood control (paper by Karamouz and Nazif), best management practices (BMPs) design and placement (papers by McGarity, Limbrunner et al., and Karamouz and Nazif), climate change adaptations (papers by Woodbury and Shoemaker and Karamouz and Nazif), total maximum daily load (TMDL) policy assessment (papers by Mirchi and Watkins and McGarity), and watershed system operations (papers by Anghileri et al. and Muste et al.). These problems are addressed through a number of real-world case studies, including both U.S. and international applications. Interestingly, a number of specific suggestions for policy and engineering design and system operations that arise from these case study problems are provided. This set of papers also demonstrates the application of the state-of-the-art systems techniques to analyzing watershed management modeling problems. Classic linear, nonlinear, and dynamic programming models are still useful and exhibit potential for}, number={5}, journal={JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT}, author={Cai, Ximing and Vogel, Richard and Ranjithan, Ranji}, year={2013}, month={Sep}, pages={461–463} }
 @article{kumar_brill_mahinthakumar_ranjithan_2012, title={Contaminant source characterization in water distribution systems using binary signals}, volume={14}, ISSN={1464-7141 1465-1734}, url={http://dx.doi.org/10.2166/hydro.2012.073}, DOI={10.2166/hydro.2012.073}, abstractNote={This paper presents a simulation–optimization-based method for identification of contamination source characteristics in a water distribution system using filtered data from threshold-based binary water quality signals. The effects of quality and quantity of the data on the accuracy of the source identification methodology are investigated. This study also addresses the issue of non-uniqueness in contaminant source identification under various data availability conditions. To establish the robustness and applicability of the methodology, numerous scenarios are investigated for a wide range of contamination incidents associated with two different networks. Results indicate that, even though use of lower resolution sensors lead to more non-unique solutions, the true source location is always included among these solutions.}, number={3}, journal={Journal of Hydroinformatics}, publisher={IWA Publishing}, author={Kumar, Jitendra and Brill, E. Downey and Mahinthakumar, G. and Ranjithan, S. Ranji}, year={2012}, month={Jul}, pages={585–602} }
 @article{liu_zechman_mahinthakumar_ranji ranjithan_2012, title={Coupling of logistic regression analysis and local search methods for characterization of water distribution system contaminant source}, volume={25}, ISSN={0952-1976}, url={http://dx.doi.org/10.1016/j.engappai.2011.10.009}, DOI={10.1016/j.engappai.2011.10.009}, abstractNote={Accidental or intentional drinking water contamination has long been and remains a major threat to water security throughout the world. An inverse problem can be constructed, given sensor measurements in a water distribution system (WDS), to identify the contaminant source characteristics by integrating a WDS simulation model with an optimization method. However, this approach requires numerous compute-intensive simulation runs to evaluate potential solutions; thus, determining the best source characteristic within a reasonable computational time is challenging. In this paper, we describe the development of a WDS contamination characterization algorithm by coupling a statistical model with a heuristic search method. The statistical model is used to identify potential source locations of contamination and a local search aims at further refining contaminant source characteristics. Application of the proposed approach to two illustrative example water distribution networks demonstrates its capability of adaptively discovering contaminant source characteristics as well as evaluating the degree of non-uniqueness of solutions. The results also showed that the local search as an optimizer has better performance than a standard evolutionary algorithm (EA).}, number={2}, journal={Engineering Applications of Artificial Intelligence}, publisher={Elsevier BV}, author={Liu, Li and Zechman, Emily M. and Mahinthakumar, G. and Ranji Ranjithan, S.}, year={2012}, month={Mar}, pages={309–316} }
 @article{mirghani_zechman_ranjithan_mahinthakumar_2012, title={Enhanced Simulation-Optimization Approach Using Surrogate Modeling for Solving Inverse Problems}, volume={13}, ISSN={1527-5922 1527-5930}, url={http://dx.doi.org/10.1080/15275922.2012.702333}, DOI={10.1080/15275922.2012.702333}, abstractNote={This study investigates and discusses groundwater system characterization problem utilizing surrogate modeling. In this inverse problem, the contaminant signals at monitoring wells are recorded to recreate the pollution profiles. In this study, simulation-optimization approach is a technique utilized to solve inverse problems by formulating them as an optimization model, where evolutionary computation algorithms are used to perform the search. In this approach, the partial differential equations (PDE) groundwater transport simulation model is solved iteratively during the evolutionary search, which in general can be computationally expensive since thousands of simulation model evaluations will be evaluated. To overcome this limitation, the simulation model is replaced by a surrogate model, which is computationally much faster than the simulation model and yet is relatively accurate. Artificial neural networks (ANN) is used to construct surrogate models that provide acceptable accuracy performances. The ANN surrogate model, which replaces the PDE groundwater transport simulation model, is then coupled with a genetic algorithm (GA) search procedure to solve the source identification problem. The results will present the quality solution of the ANN surrogate model versus the groundwater simulation model, the solution of the inverse problem for different experiment scenarios and finally a timing study analysis conducted to measure the surrogate model performance.}, number={4}, journal={Environmental Forensics}, publisher={Informa UK Limited}, author={Mirghani, Baha Y. and Zechman, Emily M. and Ranjithan, Ranji S. and Mahinthakumar, G. (Kumar)}, year={2012}, month={Jan}, pages={348–363} }
 @article{liu_zechman_mahinthakumar_ranji ranjithan_2012, title={Identifying contaminant sources for water distribution systems using a hybrid method}, volume={29}, ISSN={1028-6608 1029-0249}, url={http://dx.doi.org/10.1080/10286608.2012.663360}, DOI={10.1080/10286608.2012.663360}, abstractNote={The rapid discovery of the contaminant source in a water distribution system (WDS) is vital for generating an efficient control strategy during a contamination event. An inverse problem can be constructed, given sensor measurements in a WDS, to identify the contaminant source characteristics by integrating a WDS simulation model with an optimisation method. However, this approach requires numerous compute-intensive simulation runs to evaluate potential solutions. This paper reports the findings of an investigation by introducing a hybrid method for the real-time characterisation of a contaminant source. This new method integrates a simulation-optimisation approach with a logistic regression and a local improvement method to expedite the convergence and possibly solve the problem quickly. The results of numerical experiments on two example WDS networks demonstrate the efficiency of the proposed hybrid method for contaminant source characterisation. Effects of various hybrid strategies on the algorithm performance are discussed.}, number={2}, journal={Civil Engineering and Environmental Systems}, publisher={Informa UK Limited}, author={Liu, Li and Zechman, Emily M. and Mahinthakumar, G. and Ranji Ranjithan, S.}, year={2012}, month={Jun}, pages={123–136} }
 @article{hygh_decarolis_hill_ranji ranjithan_2012, title={Multivariate regression as an energy assessment tool in early building design}, volume={57}, ISSN={0360-1323}, url={http://dx.doi.org/10.1016/j.buildenv.2012.04.021}, DOI={10.1016/j.buildenv.2012.04.021}, abstractNote={This paper presents a new modeling approach to quantify building energy performance in early design stages. Building simulation models can accurately quantify building energy loads, but are not amenable to the early design stages when architects need an assessment tool that can provide rapid feedback based on changes to high level design parameters. We utilize EnergyPlus, an existing whole building energy simulation program, within a Monte Carlo framework to develop a multivariate linear regression model based on 27 building parameters relevant to the early design stages. Because energy performance is sensitive to building size, geometry, and location, we model a medium-sized, rectangular office building and perform the regression in four different cities—Miami, Winston-Salem, Albuquerque, and Minneapolis—each representing a different climate zone. With the exception of heating in Miami, all R2 values obtained from the multivariate regressions exceeded 96%, which indicates an excellent fit to the EnergyPlus simulation results. The analysis suggests that a linear regression model can serve as the basis for an effective decision support tool in place of energy simulation models during early design stages. In addition, we present standardized regression coefficients to quantify the sensitivity of heating, cooling, and total energy loads to building design parameters across the four climate zones. The standardized regression coefficients can be used directly by designers to target building design parameters in early design that drive energy performance.}, journal={Building and Environment}, publisher={Elsevier BV}, author={Hygh, Janelle S. and DeCarolis, Joseph F. and Hill, David B. and Ranji Ranjithan, S.}, year={2012}, month={Nov}, pages={165–175} }
 @inproceedings{sreepathi_brill_ranjithan_mahinthakumar_2012, title={Parallel Multi-Swarm Optimization Framework for Search Problems in Water Distribution Systems}, ISBN={9780784412312}, url={http://dx.doi.org/10.1061/9780784412312.323}, DOI={10.1061/9780784412312.323}, abstractNote={Population based heuristic search methods such as evolutionary algorithms (EA) and particle swarm optimization (PSO) methods are widely used for solving optimization problems especially when classical techniques are inadequate. A parallel optimization framework using multiple concurrent particle swarms is developed and applied to water distribution problems. Details of the enabling framework that couples the optimization methods with a parallel simulator built around EPANET will be discussed. In addition, algorithmic and computational performance results using ORNL’s and ANL’s leadership class parallel architectures will be presented for leakage detection and contaminant source characterization problems for two water distribution networks with 1,834 and 12,457 nodes respectively.}, booktitle={World Environmental and Water Resources Congress 2012}, publisher={American Society of Civil Engineers}, author={Sreepathi, Sarat and Brill, Downey and Ranjithan, Ranji and Mahinthakumar, Gnanamanikam (Kumar)}, year={2012}, month={May} }
 @inproceedings{barandouzi_mahinthakumar_ranjithan_brill_2012, title={Probabilistic Mapping of Water Leakage Characterizations Using a Bayesian Approach}, ISBN={9780784412312}, url={http://dx.doi.org/10.1061/9780784412312.326}, DOI={10.1061/9780784412312.326}, abstractNote={Water Distribution Systems are one of the most substantial and vulnerable part of civil infrastructure systems. For the reason that many large water distribution systems are old, which results in more leakage and expenses (e.g., increasing pump head, pipe burst, constituents’ replacement), a significant portion of water produced by the utilities never passes through the consumers’ meters. Due to the complex nature and vast spatial extent of a water distribution system it may be difficult for the utility personnel to identify and fix the leaks, therefore it is imperative to develop software frameworks for modeling and analyzing leakage in water distribution system during ordinary operational conditions as well as unexpected events. In this paper a Bayesian approach with Markov chain Monte Carlo method is implemented to map probabilistic characterizations of water leakage. If for this purpose physical parameters such as pipe vintage, material, and loading are available, they can be are used to develop prior information; otherwise, a uniform prior may be assumed. Routinely measured water quality, pressure, and flow measurements together with the uncertainty in demand are used to develop the likelihood function. The analyses are facilitated through the EPANET water distribution simulation tool. The efficiency and versatility of the proposed methodology is examined using water distribution network.}, booktitle={World Environmental and Water Resources Congress 2012}, publisher={American Society of Civil Engineers}, author={Barandouzi, M. A. and Mahinthakumar, G. and Ranjithan, R. and Brill, E. D.}, year={2012}, month={May} }
 @article{liu_ranjithan_mahinthakumar_2011, title={Contamination Source Identification in Water Distribution Systems Using an Adaptive Dynamic Optimization Procedure}, volume={137}, ISSN={["1943-5452"]}, DOI={10.1061/(asce)wr.1943-5452.0000104}, abstractNote={Contamination source identification involves the characterization of the contaminant source based on observations that stream from a set of sensors in a water distribution system (WDS). The streaming data can be processed adaptively to provide an estimate of the source characteristics at any time once the contamination event is detected. In this paper, an adaptive dynamic optimization technique (ADOPT) is proposed for providing a real-time response to a contamination event. A new multiple population–based search that uses an evolutionary algorithm (EA) is investigated. To address nonuniqueness in the initial stages of the search and prevent premature convergence of the EA to an incorrect solution, the multiple populations are designed to maintain a set of alternative solutions that represent various nonunique solutions. As more observations are added, the EA solutions not only migrate to better solution states but the number of solutions decreases as the degree of nonuniqueness diminishes. This new algori...}, number={2}, journal={JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT}, author={Liu, Li and Ranjithan, S. Ranji and Mahinthakumar, G.}, year={2011}, pages={183–192} }
 @article{liu_sankarasubramanian_ranjithan_2011, title={Logistic regression analysis to estimate contaminant sources in water distribution systems}, volume={13}, ISSN={1464-7141 1465-1734}, url={http://dx.doi.org/10.2166/hydro.2010.106}, DOI={10.2166/hydro.2010.106}, abstractNote={Accidental or intentional contamination in a water distribution system (WDS) has recently attracted attention due to the potential hazard to public health and the complexity of the contaminant characteristics. The accurate and rapid characterization of contaminant sources is necessary to successfully mitigate the threat in the event of contamination. The uncertainty surrounding the contaminants, sensor measurements and water consumption underscores the importance of a probabilistic description of possible contaminant sources. This paper proposes a rapid estimation methodology based on logistic regression (LR) analysis to estimate the likelihood of any given node as a potential source of contamination. Not only does this algorithm yield location-specific probability information, but it can also serve as a prescreening step for simulation–optimization methods by reducing the decision space and thus alleviating the computational burden. The applications of this approach to two example water networks show that it can efficiently rule out numerous nodes that do not yield contaminant concentrations to match the observations. This elimination process narrows down the search space of the potential contamination locations. The results also indicate that the proposed method efficiently yields a good estimation even when some noise is incorporated into the measurements and demand values at the consumption nodes.}, number={3}, journal={Journal of Hydroinformatics}, publisher={IWA Publishing}, author={Liu, Li and Sankarasubramanian, A. and Ranjithan, S. Ranji}, year={2011}, month={Jul}, pages={545–557} }
 @article{levis_barlaz_tayebali_ranjithan_2011, title={Quantifying the Greenhouse Gas Emission Reductions Associated with Recycling Hot Mix Asphalt}, volume={12}, ISSN={1468-0629 2164-7402}, url={http://dx.doi.org/10.1080/14680629.2011.9690352}, DOI={10.1080/14680629.2011.9690352}, abstractNote={ABSTRACT Market based policies to reduce greenhouse gas emissions have become increasingly popular in the last decade. These policies provide economic incentives for reducing greenhouse gas emissions. A life-cycle inventory model was developed to evaluate three alternatives for the management of waste hot mix asphalt (HMA) including, (1) recycling as new aggregate, (2) recycling as new HMA, and (3) disposal in a landfill. Global warming potential, environmental emissions, and total energy use were quantified for each management alternative. The recycling of used asphalt into new HMA results in a reduction of 16 kg CO2e compared to landfilling. Recycling used HMA as aggregate reduced GHG emissions by 9 kg CO2e A Monte Carlo analysis on the alternatives showed that the range of reduction for recycling as HMA was 12 to 26 kg CO2e and for recycling as aggregate 6 to 11 kg CO2e.}, number={1}, journal={Road Materials and Pavement Design}, publisher={Informa UK Limited}, author={Levis, James W. and Barlaz, Morton A. and Tayebali, Akhtar and Ranjithan, S. Ranji}, year={2011}, month={Jan}, pages={57–77} }
 @article{jung_ranjithan_mahinthakumar_2011, title={Subsurface characterization using a D-optimality based pilot point method}, volume={13}, ISSN={["1465-1734"]}, DOI={10.2166/hydro.2010.111}, abstractNote={Detailed hydraulic conductivity estimation is a difficult problem as the number of direct measurements available at a typical field site is relatively few and sparse. A common approach to estimate hydraulic conductivity is to combine direct hydraulic conductivity measurements with secondary measurements such as hydraulic head and tracer concentrations in an inverse modeling approach. Even with secondary measurements this may constitute an underdetermined (or over-parameterized) inverse problem giving rise to ‘non-unique’ and incorrect estimates. One approach to reduce over-parameterization is to estimate hydraulic conductivity at a few carefully chosen points called ‘pilot points’ (i.e. reduction in parameter space). This paper develops a D-optimality based criterion method (DBM) for pilot point selection and tests its effectiveness for estimating hydraulic conductivity fields using several synthetic cases. Results show that the selected pilot points using this approach lead to a more accurate hydraulic conductivity characterization than either random or sequential pilot point location selection methods.}, number={4}, journal={JOURNAL OF HYDROINFORMATICS}, author={Jung, Yong and Ranjithan, Ranji S. and Mahinthakumar, G.}, year={2011}, pages={775–793} }
 @article{tryby_mirghani_mahinthakumar_ranjithan_2010, title={A SOLUTION FRAMEWORK FOR ENVIRONMENTAL CHARACTERIZATION PROBLEMS}, volume={24}, ISSN={["1741-2846"]}, DOI={10.1177/1094342009350886}, abstractNote={ This paper describes experiences developing a grid-enabled framework for solving environmental inverse problems. The solution approach taken here couples environmental simulation models with global search methods and requires readily available computational resources of the grid for computational tractability. The solution framework developed by the authors uses a master—worker strategy for task distribution and a pool for task mapping. Solution and computational performance results are presented for groundwater source identification and release history reconstruction problems. They indicate that high-quality solutions and significant raw performance improvements were attained for a deployment of the solution framework on the TeraGrid. }, number={3}, journal={INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS}, author={Tryby, M. E. and Mirghani, B. Y. and Mahinthakumar, G. K. and Ranjithan, S. R.}, year={2010}, month={Aug}, pages={265–283} }
 @article{liu_ranjithan_2010, title={An adaptive optimization technique for dynamic environments}, volume={23}, ISSN={["1873-6769"]}, DOI={10.1016/j.engappai.2010.01.007}, abstractNote={The use of evolutionary algorithms (EAs) is beneficial for addressing optimization problems in dynamic environments. The objective function for such problems changes continually; thus, the optimal solutions likewise change. Such dynamic changes pose challenges to EAs due to the poor adaptability of EAs once they have converged. However, appropriate preservation of a sufficient level of individual diversity may help to increase the adaptive search capability of EAs. This paper proposes an EA-based Adaptive Dynamic OPtimization Technique (ADOPT) for solving time-dependent optimization problems. The purpose of this approach is to identify the current optimal solution as well as a set of alternatives that is not only widespread in the decision space, but also performs well with respect to the objective function. The resultant solutions may then serve as a basis solution for the subsequent search while change is occurring. Thus, such an algorithm avoids the clustering of individuals in the same region as well as adapts to changing environments by exploiting diverse promising regions in the solution space. Application of the algorithm to a test problem and a groundwater contaminant source identification problem demonstrates the effectiveness of ADOPT to adaptively identify solutions in dynamic environments.}, number={5}, journal={ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE}, author={Liu, Li and Ranjithan, S. Ranji}, year={2010}, month={Aug}, pages={772–779} }
 @inproceedings{jin_wang_ranjithan_2010, title={Bayesian Inference of Groundwater Contamination Source}, ISBN={9780784411148}, url={http://dx.doi.org/10.1061/41114(371)100}, DOI={10.1061/41114(371)100}, abstractNote={Lots of uncertainty exists in the groundwater modeling, e.g. hydraulic conductivity, measurement variance and the model structure error. Monte Carlo simulation of flow model allows the input uncertainty onto the model predictions of concentration measurements at monitoring sites. Bayesian approach provides the advantage to update estimation. This work proposes a dynamic framework in contamination source identification of groundwater. Markov Chain Monte Carlo(MCMC) is being applied to infer the possible location and magnitude of contamination source. Unlike other inverse-problem approach to provide single but maybe untrue solution, the MCMC algorithm provides distribution over estimated parameters.}, booktitle={World Environmental and Water Resources Congress 2010}, publisher={American Society of Civil Engineers}, author={Jin, Xin and Wang, Hui and Ranjithan, Ranji S.}, year={2010}, month={May} }
 @inproceedings{kumar_sreepathi_brill_ranjithan_mahinthakumar_2010, title={Detection of Leaks in Water Distribution System Using Routine Water Quality Measurements}, ISBN={9780784411148}, url={http://dx.doi.org/10.1061/41114(371)426}, DOI={10.1061/41114(371)426}, abstractNote={Water distributions systems are primary means of safe drinking water supply to the public. Water produced and delivered to the distribution system is intended for the customer. However, a significant amount of the water is lost in the distribution system before even reaching the customers. Water customers are metered for the usage at end connection but a significant portion of water produced by the utilities never passes through the meters. This leads to wastage of valuable water and loss of revenues for the utilities. The occurrence of leaks depends on the factors like material, composition, age and joining methods of the distribution systems components. Due to the complex nature and vast spatial extent of a water distribution system it may be difficult for the utility personnel to identify and fix the leaks. Traditionally, the method of inverse transient analysis (ITA) has been used by the researchers for identifying the leaks in a distribution system. While transient analysis is an efficient method for leak detection, it often requires that a series of hydraulic transients (or pressure pulses) be injected into the system in order to detect the leaks (e.g., controlled opening/closing of a fire hydrant). In contrast to ITA, this work attempts to use routinely measured water quality and pressure measurements for the detection of leaks. Distribution systems are routinely monitored for several water quality parameters such as Chlorine, pH, and turbidity. Water loss due to any leaks present in the system would impact the flow characteristics of the system and would have an impact on the water quality. In this study a methodology has been developed to use the water quality data along with available pressure measurements for the improved detection of leaks in a water distribution system. Leak detection is formulated as an inverse problem and solved using a simulation-optimization approach.}, booktitle={World Environmental and Water Resources Congress 2010}, publisher={American Society of Civil Engineers}, author={Kumar, Jitendra and Sreepathi, Sarat and Brill, E. Downey and Ranjithan, Ranji and Mahinthakumar, G.}, year={2010}, month={May} }
 @article{mirghani_tryby_ranjithan_karonis_mahinthakumar_2010, title={Grid-Enabled Simulation-Optimization Framework for Environmental Characterization}, volume={24}, ISSN={["1943-5487"]}, DOI={10.1061/(asce)cp.1943-5487.0000052}, abstractNote={Many engineering and environmental problems that involve the determination of unknown system characteristics from observation data can be categorized as inverse problems. A common approach undertaken to solve such problems is the simulation-optimization approach where simulation models are coupled with optimization or search methods. Simulation-optimization approaches, particularly in environmental characterization involving natural systems, are computationally expensive due to the complex three-dimensional simulation models required to represent these systems and the large number of such simulations involved. Emerging grid computing environments (e.g., TeraGrid) show promise for improving the computational tractability of these approaches. However, harnessing grid resources for most computational applications is a nontrivial problem due to the complex hierarchy of heterogeneous and geographically distributed resources involved in a grid. This paper reports and discusses the development and evaluation of ...}, number={6}, journal={JOURNAL OF COMPUTING IN CIVIL ENGINEERING}, author={Mirghani, Baha Y. and Tryby, Michael E. and Ranjithan, Ranji S. and Karonis, Nicholas T. and Mahinthakumar, Kumar G.}, year={2010}, pages={488–498} }
 @inproceedings{kumar_brill_mahinthakumar_ranjithan_2010, title={Identification of Reactive Contaminant Sources in a Water Distribution System under the Conditions of Data Uncertainties}, ISBN={9780784411148}, url={http://dx.doi.org/10.1061/41114(371)442}, DOI={10.1061/41114(371)442}, abstractNote={Water distribution systems are designed for fast and efficient transport of the drinking water and mixing of chlorine to maintain the required disinfectant levels in the system. Thus, any contaminant if injected in the system would also spread quickly through the network and can have serious impact on public health if consumed. Contaminant injected during any intentional contamination event can be chemical or biological, the nature of which may remain unknown. Practically it's not possible to monitor any system for the presence of all possible chemical or biological contaminants. However, the distribution systems are routinely monitored for several water quality parameters like chlorine, pH, etc. Any contaminant injected in the system would react with water and chlorine leading to the increased degradation of the chlorine levels in the system. In our past work we developed methodology to use routine chlorine measurements as a surrogate to identify a contamination event in a WDS. An evolutionary algorithm based approach simulation-optimization was developed to identify the contaminant source characteristics (i.e., location of the contaminant source, time of start of injection and injection pattern) during a contamination event under conditions of uncertainty about the reaction kinetics of the contaminant in the system. The investigation was extended to study the source characterization problem under different uncertain reaction conditions. We present here a detailed analysis of source characterization problem for the reactive contaminants and the simulation-optimization methodology developed. Case studies carried out on a number of water distributions systems will be reported.}, booktitle={World Environmental and Water Resources Congress 2010}, publisher={American Society of Civil Engineers}, author={Kumar, Jitendra and Brill, E. Downey and Mahinthakumar, G. and Ranjithan, Ranji}, year={2010}, month={May} }
 @article{tryby_propato_ranjithan_2010, title={Monitoring Design for Source Identification in Water Distribution Systems}, volume={136}, ISSN={["0733-9496"]}, DOI={10.1061/(asce)wr.1943-5452.0000080}, abstractNote={The design of sensor networks for monitoring contaminants in water distribution systems is currently an active area of research. Much of the effort has been directed at the contamination detection problem and the expression of public health protection objectives. Monitoring networks once they are in place, however, are likely to be used to gather monitoring data for source inversion as well. Thus, the design of these networks with the unique objectives associated with source inversion problems in mind is a necessity. Source inversion problems in water distribution systems are inherently underdetermined and exhibit solution nonuniqueness; and moreover, the structure of the errors associated with a solution are a function of monitoring observations. Optimal inverse experiment design is investigated as an approach for improving solution quality. The approach involves the selection of monitoring locations that are best suited to the generation of a well-conditioned source identification inverse problem. The m...}, number={6}, journal={JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT-ASCE}, author={Tryby, Michael E. and Propato, Marco and Ranjithan, S. Ranji}, year={2010}, pages={637–646} }
 @inproceedings{wang_arumugam_ranjithan_2010, title={Seamless Integration of Weather and Climate Information in Developing Operational Streamflow Forecasts}, ISBN={9780784411148}, url={http://dx.doi.org/10.1061/41114(371)470}, DOI={10.1061/41114(371)470}, abstractNote={Various atmospheric and ocean conditions, such as El-Nino Southern Oscillation, affect the monthly and seasonal streamflow potential in a given region. Similarly, at daily to weekly time scales, well known oscillations such as PNA (Pacific-North America) influence the skill of weather forecasts. Thus, to develop bi-weekly streamflow forecasts, we propose an algorithm that combines streamflow forecasts downscaled from medium-range weather information and the disaggregated bi-weekly streamflow forecasts from climate information. For basins, whose monthly and seasonal streamflows are primarily SST driven with limited skill in predicting the bi-weekly weather, such a combination would be expected to yield benefits in developing better weekly streamflow forecasts. To demonstrate this algorithm, we first analyze using a synthetic study that consider streamflow forecasts having different skills at bi-weekly and monthly time scales and combine both of them using the proposed algorithm. Preliminary investigations clearly show that combining medium range weather and climate information could improve the skill in predicting the bi-weekly precipitation/streamflow forecasts for the basin. Findings from the synthetic study is also validated by combining retrospective bi-weekly weather forecasts and the disaggregated bi-weekly forecasts obtained from the retrospective climate forecasts for various regions in the country.}, booktitle={World Environmental and Water Resources Congress 2010}, publisher={American Society of Civil Engineers}, author={Wang, Hui and Arumugam, Sankarasubramanian and Ranjithan, Ranji S.}, year={2010}, month={May} }
 @inproceedings{li_arumugam_ranjithan_2010, title={Utility of Climate Forecasts in Promoting Inter-Basin Transfer in the North Carolina Triangle Area}, ISBN={9780784411148}, url={http://dx.doi.org/10.1061/41114(371)270}, DOI={10.1061/41114(371)270}, abstractNote={Droughts experienced by regional water supply systems often result due to reduced streamflow/precipitation potential which could occur due to varying exogenous climatic conditions such as tropical sea surface temperature (SST). Similarly, water supply systems can also experience frequent shortages in supply due to increased water demand resulting from urbanization and population growth in the region. The goal of this study is to identify a sustainable way of managing triangle area's two major reservoir systems while in the meantime improving the water supply reliability for its urban area. In this study, streamflow forecasts downscaled from climate forecasts for the winter season is developed to explore the potential for inter-basin transfer between Falls Lake of the Neuse River basin and Jordan Lake in the Cape Fear River basin. Using the 3-month ahead ensembles of streamflow forecasts, the reservoir simulation model estimates the probability of meeting the end of the season target storage for the two systems. Comparing these two probabilities, various scenarios of inter-basin transfers between the two systems are analyzed in such a way that the water quality releases from both systems are not endangered. Results show that by introducing inter-basin transfer, the reliability of the water supply for the triangle area could be increased, which would help in developing regional drought management strategies.}, booktitle={World Environmental and Water Resources Congress 2010}, publisher={American Society of Civil Engineers}, author={Li, Weihua and Arumugam, Sankarasubramanian and Ranjithan, Ranji S.}, year={2010}, month={May} }
 @inproceedings{liu_brill_mahinthakumar_ranjithan_2009, title={A Hybrid Heuristic Search Approach for Contaminant Source Characterization}, ISBN={9780784410363}, url={http://dx.doi.org/10.1061/41036(342)63}, DOI={10.1061/41036(342)63}, abstractNote={The rapid discovery of the contaminant source and its mass loading characteristics in a water distribution system (WDS) is vital for generating an efficient control strategy during a contamination event. Previous work on the Adaptive Dynamic Optimization Technique (ADOPT), which was developed as an Evolution Strategy (ES) based procedure, presents an approach to estimate the source characteristics adaptively, given dynamically updated observation data. Although this simulation-optimization approach is promising, it is computationally expensive, which poses challenges in the context of real-time solutions. This paper reports the findings of an investigation that builds upon the prior work by introducing a hybrid heuristic search method for the real-time characterization of a contaminant source. This new method integrates the ES-based ADOPT with a logistic regression (LR) analysis and a local improvement method to expedite the convergence and possibly solve the problem quickly. As a prescreening technique, a LR analysis step is performed prior to ADOPT; this step reduces the search space by eliminating unnecessary source nodes as potential source locations. Then, a local search (LS) approach is embedded into some of the algorithmic steps in ADOPT to serve as a postscreening step that potentially speeds up the convergence in localized regions in the solution space. Numerical experiments for the proposed hybrid approach are performed on an example water distribution network, and the results are compared with those of the standard implementation of ADOPT.}, booktitle={World Environmental and Water Resources Congress 2009}, publisher={American Society of Civil Engineers}, author={Liu, Li and Brill, E. Downey and Mahinthakumar, G. (Kumar) and Ranjithan, S. Ranji}, year={2009}, month={May} }
 @article{jin_mahinthakumar_zechman_ranjithan_2009, title={A genetic algorithm-based procedure for 3D source identification at the Borden emplacement site}, volume={11}, ISSN={1464-7141 1465-1734}, url={http://dx.doi.org/10.2166/hydro.2009.002}, DOI={10.2166/hydro.2009.002}, abstractNote={Finding the location and concentration of groundwater contaminant sources typically requires the solution of an inverse problem. A parallel hybrid optimization framework that uses genetic algorithms (GA) coupled with local search approaches (GA-LS) has been developed previously to solve groundwater inverse problems. In this study, the identification of an emplaced source at the Borden site is carried out as a test problem using this optimization framework by using a Real Genetic Algorithm (RGA) as the GA approach and a Nelder–Mead simplex as the LS approach. The RGA results showed that the minimum objective function did not always correspond to the minimum solution error, indicating a possible non-uniqueness issue. To address this problem, a procedure to identify maximally different starting points for LS is introduced. When measurement or model errors are non-existent or minimal it is shown that one of these starting points leads to the true solution. When these errors are significant, this procedure leads to multiple possible solutions that could be used as a basis for further investigation. Metrics of mean and standard deviation of objective function values was adopted to evaluate the possible solutions. A new selection criterion based on these metrics is suggested to find the best alternative. This suggests that this alternative generation procedure could be used to address the non-uniqueness of similar inverse problems. A potential limitation of this approach is the application to a wide class of problems, as verification has not been performed with a large number of test cases or other inverse problems. This remains a topic for future work.}, number={1}, journal={Journal of Hydroinformatics}, publisher={IWA Publishing}, author={Jin, Xin and Mahinthakumar, G. (Kumar) and Zechman, Emily M. and Ranjithan, Ranji S.}, year={2009}, month={Jan}, pages={51–64} }
 @article{mirghani_mahinthakumar_tryby_ranjithan_zechman_2009, title={A parallel evolutionary strategy based simulation–optimization approach for solving groundwater source identification problems}, volume={32}, ISSN={0309-1708}, url={http://dx.doi.org/10.1016/j.advwatres.2009.06.001}, DOI={10.1016/j.advwatres.2009.06.001}, abstractNote={Groundwater characterization involves the resolution of unknown system characteristics from observation data, and is often classified as an inverse problem. Inverse problems are difficult to solve due to natural ill-posedness and computational intractability. Here we adopt the use of a simulation–optimization approach that couples a numerical pollutant-transport simulation model with evolutionary search algorithms for solution of the inverse problem. In this approach, the numerical transport model is solved iteratively during the evolutionary search. This process can be computationally intensive since several hundreds to thousands of forward model evaluations are typically required for solution. Given the potential computational intractability of such a simulation–optimization approach, parallel computation is employed to ease and enable the solution of such problems. In this paper, several variations of a groundwater source identification problem is examined in terms of solution quality and computational performance. The computational experiments were performed on the TeraGrid cluster available at the National Center for Supercomputing Applications. The results demonstrate the performance of the parallel simulation–optimization approach in terms of solution quality and computational performance.}, number={9}, journal={Advances in Water Resources}, publisher={Elsevier BV}, author={Mirghani, Baha Y. and Mahinthakumar, Kumar G. and Tryby, Michael E. and Ranjithan, Ranji S. and Zechman, Emily M.}, year={2009}, month={Sep}, pages={1373–1385} }
 @article{far_underwood_ranjithan_kim_jackson_2009, title={Application of Artificial Neural Networks for Estimating Dynamic Modulus of Asphalt Concrete}, volume={2127}, ISSN={0361-1981 2169-4052}, url={http://dx.doi.org/10.3141/2127-20}, DOI={10.3141/2127-20}, abstractNote={ This paper presents outcomes from a research effort to develop models for estimating the dynamic modulus (| E*|) of hot-mix asphalt (HMA) layers on long-term pavement performance test sections. The goal of the work is the development of a new, rational, and effective set of dynamic modulus | E*| predictive models for HMA mixtures. These predictive models use artificial neural networks (ANNs) trained with the same set of parameters used in other popular predictive equations: the modified Witczak and Hirsch models. The main advantage of using ANNs for predicting | E*| is that an ANN can be created for different sets of variables without knowing the form of the predictive relationship a priori. The primary disadvantage of ANNs is the difficulty in predicting responses when the inputs are outside of the training database (i.e., extrapolation). To overcome this shortcoming, a large data set that covers the complete range of potential input conditions is needed. For this study, modulus values from multiple mixtures and binders were required and were assembled from existing national efforts and from data obtained at North Carolina State University. The data consisted of measured moduli from both modified and unmodified mixtures from numerous geographical locations across the United States. Prediction models were developed by using a portion of the data from these databases and then verified by using the remaining data in the databases. When these new ANN models are used, the results show that the predicted and measured | E*| values are in close agreement. }, number={1}, journal={Transportation Research Record: Journal of the Transportation Research Board}, publisher={SAGE Publications}, author={Far, Maryam Sadat Sakhaei and Underwood, B. Shane and Ranjithan, S. Ranji and Kim, Y. Richard and Jackson, Newton}, year={2009}, month={Jan}, pages={173–186} }
 @inproceedings{kumar_brill_mahinthakumar_ranjithan_2009, title={Characterizing Reactive Contaminant Sources in a Water Distribution System}, ISBN={9780784410363}, url={http://dx.doi.org/10.1061/41036(342)65}, DOI={10.1061/41036(342)65}, abstractNote={Accurate knowledge of the characteristics of the contamination source during a contamination event is necessary for development of any mitigation and control strategy. Contaminant injected in a system is most likely to be reactive with chlorine; however, it is impractical for water quality monitoring systems to be able to monitor for the presence of all possible contaminants. In any distribution system, chlorine levels and other water quality parameters (pH, conductance, etc.) are routinely monitored to maintain the prescribed disinfection capacity. Any reactive contaminant would affect the chlorine levels resulting in deviations in the expected chlorine levels from those expected under normal operating conditions. Anomalies in the chlorine concentration from that of the expected value can be used as a surrogate to characterize the contaminant source in the system. In the absence of knowing the reactive characteristics of the contaminants, the location of injection, and injection pattern, source identification becomes a difficult problem to solve. Source identification can be posed as an inverse problem. In earlier work authors investigated the effect of the order of reaction kinetics of the contaminant with chlorine and its impact on source identification problem assuming the reaction kinetics to be known. That work is extended to investigate a methodology to address the source identification problem based on chlorine measurements, and the effects of different uncertain contamination conditions. Findings from a range of scenarios will be presented and discussed.}, booktitle={World Environmental and Water Resources Congress 2009}, publisher={American Society of Civil Engineers}, author={Kumar, Jitendra and Brill, E. Downey and Mahinthakumar, G and Ranjithan, Ranji}, year={2009}, month={May} }
 @article{vankayala_sankarasubramanian_ranjithan_mahinthakumar_2009, title={Contaminant Source Identification in Water Distribution Networks Under Conditions of Demand Uncertainty}, volume={10}, ISSN={["1527-5930"]}, DOI={10.1080/15275920903140486}, abstractNote={Water distribution systems are susceptible to accidental and intentional chemical or biological contamination that could result in adverse health impact to the consumers. This study focuses on a water distribution forensics problem, contaminant source identification, subject to water demand uncertainty. Due to inherent variability in water consumption levels, demands at consumer nodes remain one of the major sources of uncertainty. In this research, the nodal demands are considered to be stochastic in nature and are varied using Gaussian and Autoregressive models. A hypothetical source identification problem is constructed by simulating observations at the sensor nodes from an arbitrary contaminant source. A simulation-optimization approach is used to solve the source identification problem with EPANET tool as the simulator and Genetic Algorithm (GA) as the optimizer. The goal is to find the source location and concentration by minimizing the difference between the simulated and observed concentrations at the sensor nodes. Two variations of GA, stochastic GA and noisy GA are applied to the same problem for comparison. Results show that noisy GA is more robust and is less computationally expensive than stochastic GA in solving the source identification problem. Moreover, the autoregressive demand uncertainty model better represents the uncertainty in the source identification process than the Gaussian model.}, number={3}, journal={ENVIRONMENTAL FORENSICS}, author={Vankayala, Praveen and Sankarasubramanian, A. and Ranjithan, S. Ranji and Mahinthakumar, G.}, year={2009}, pages={253–263} }
 @article{zechman_ranjithan_2009, title={Evolutionary Computation-Based Methods for Characterizing Contaminant Sources in a Water Distribution System}, volume={135}, ISSN={0733-9496 1943-5452}, url={http://dx.doi.org/10.1061/(asce)0733-9496(2009)135:5(334)}, DOI={10.1061/(ASCE)0733-9496(2009)135:5(334)}, abstractNote={The area of systematic identification of contamination sources in water distribution systems is in its infancy and is rapidly growing. The real water distribution network problem poses many challenges that current methods usually assume away to facilitate manageable method development and testing. Current methods may not readily and efficiently address issues, such as multiple sources, unknown contamination types with different reaction kinetics, use of different types of sensors with varying degree of resolution, dynamically varying demand and sensor information, and uncertainty and errors in the data and measurements. With the aim of addressing these imminent challenges, this paper reports the findings of an ongoing research investigation that develops and tests an evolutionary algorithm-based flexible and generic procedure, which is structured within a simulation-optimization paradigm. This paper describes the specific implementation of the method using evolution strategies (ESs), a population-based he...}, number={5}, journal={Journal of Water Resources Planning and Management}, publisher={American Society of Civil Engineers (ASCE)}, author={Zechman, Emily M. and Ranjithan, S. Ranji}, year={2009}, month={Sep}, pages={334–343} }
 @article{kaplan_ranjithan_barlaz_2009, title={Use of Life-Cycle Analysis To Support Solid Waste Management Planning for Delaware}, volume={43}, ISSN={["0013-936X"]}, DOI={10.1021/es8018447}, abstractNote={Mathematical models of integrated solid waste management (SWM) are useful planning tools given the complexity of the solid waste system and the interactions among the numerous components that constitute the system. An optimization model was used in this study to identify and evaluate alternative plans for integrated SWM for the State of Delaware in consideration of cost and environmental performance, including greenhouse gas (GHG) emissions. The three counties in Delaware were modeled individually to identify efficient SWM plans in consideration of constraints on cost, landfill diversion requirements, GHG emissions, and the availability of alternate treatment processes (e.g., recycling, composting, and combustion). The results show that implementing a landfill diversion strategy (e.g., curbside recycling) for only a portion of the population is most cost-effective for meeting a county-specific landfill diversion target Implementation of waste-to-energy offers the most cost-effective opportunity for GHG emissions reductions.}, number={5}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, author={Kaplan, P. Ozge and Ranjithan, S. Ranji and Barlaz, Morton A.}, year={2009}, month={Mar}, pages={1264–1270} }
 @inproceedings{liu_zechman_brill, jr._mahinthakumar_ranjithan_uber_2008, title={Adaptive Contamination Source Identification in Water Distribution Systems Using an Evolutionary Algorithm-based Dynamic Optimization Procedure}, ISBN={9780784409411}, url={http://dx.doi.org/10.1061/40941(247)123}, DOI={10.1061/40941(247)123}, abstractNote={Accidental drinking water contamination has long been and remains a major threat to water security throughout the world. Consequently, contamination source identification is an important and difficult problem in the managing safety in water distribution systems. This problem involves the characterization of the contaminant source based on observations that are streaming from a set of sensors in the distribution network. Since contamination spread in a water distribution network is relatively quick and unpredictable, rapid identification of the source location and related characteristics is important to take contaminant control and containment actions. As the contaminant event unfolds, the streaming data could be processed over time to adaptively estimate the source characteristics. This provides an estimate of the source characteristics at any time after a contamination event is detected, and this estimate is continually updated as new observations become available. We pose and solve this problem using a dynamic optimization procedure that could potentially provide a real-time response. As time progresses, additional data is observed at a set of sensors, changing the vector of observations that should be predicted. Thus, the prediction error function is updated dynamically, changing the objective function in the optimization model. We investigate a new multi population-based search using an evolutionary algorithm (EA) that at any time represents the solution state that best matches the available observations. The set of populations migrates to represent updated solution states as new observations are added over time. At the initial detection period, non-uniqueness is inherent in the source-identification due to inadequate information, and, consequently, several solutions may predict similarly well. To address nonuniqueness at the initial stages of the search and prevent premature convergence of the EA to an incorrect solution, the multiple populations in the proposed methodology are designed to maintain a set of alternative solutions representing different non-unique solutions. As more observations are added, the EA solutions not only migrate to better solution states, but also reduce the number of solutions as the degree of non-uniqueness diminishes. This new dynamic optimization algorithm adaptively converges to the best solution(s) to match the observations available at any time. The new method will be demonstrated for a contamination source identification problem in an illustrative water distribution network.}, booktitle={Water Distribution Systems Analysis Symposium 2006}, publisher={American Society of Civil Engineers}, author={Liu, Li and Zechman, Emily M. and Brill, Jr., E. Downey and Mahinthakumar, G. and Ranjithan, S. and Uber, James}, year={2008}, month={Mar} }
 @inproceedings{zechman_brill, jr._mahinthakumar_ranjithan_uber_2008, title={Addressing Non-uniqueness in a Water Distribution Contaminant Source Identification Problem}, ISBN={9780784409411}, url={http://dx.doi.org/10.1061/40941(247)126}, DOI={10.1061/40941(247)126}, abstractNote={The source of contamination in a water distribution system may be identified through a simulation-optimization approach. The optimization method searches for the contaminant source characteristics by iteratively estimating the contaminant plume concentrations until they match observations at sensors. The amount of information available for characterizing the source depends on the number and spatial locations of the sensors, as well as on the temporally varying stream of sensed data. The accuracy of the source characterization depends on the amount of observations available. A major factor affecting this accuracy is the degree of non-uniqueness present in the problem, which may cause misidentification of the source characteristics. As more sensors are added to the network, the non-uniqueness may be reduced and a unique solution may be identified. Thus, a key consideration when solving these problems is to assess whether the solution identified is unique, and if not, what other possible solutions are present. A systematic search for a set of alternatives that are maximally different in solution characteristics can be used to address and quantify non-uniqueness. For example, if the most different set of solutions that are identified by a search procedure are very similar, then that solution will be considered as the unique solution with a higher degree of certainty. Alternatively, identification of a set of maximally different solutions that vary widely in solution characteristics will indicate that nonuniqueness is present in the problem, and the range of solutions can be used as a general representation of the amount of non-uniqueness. This paper investigates the use of evolutionary algorithm (EA)-based alternatives generation procedures to quantify and address non-uniqueness present in a contaminant source identification problem for a water distribution network. As additional sensors may decrease the amount of non-uniqueness, several sensor configurations will be tested to investigate and quantify the improvement in uniqueness as more information is used in the source characterization.}, booktitle={Water Distribution Systems Analysis Symposium 2006}, publisher={American Society of Civil Engineers}, author={Zechman, Emily M. and Brill, Jr., E. Downey and Mahinthakumar, G. and Ranjithan, S. and Uber, James}, year={2008}, month={Mar} }
 @article{lacroix_kim_ranjithan_2008, title={Backcalculation of Dynamic Modulus from Resilient Modulus of Asphalt Concrete with an Artificial Neural Network}, ISSN={["2169-4052"]}, DOI={10.3141/2057-13}, abstractNote={ The NCHRP Project 1-37A Guide for Mechanistic–Empirical Design of New and Rehabilitated Pavement Structures introduces the dynamic modulus (|E*|) as the material property for the characterization of hot-mix asphalt mixtures. This is a significant change from the resilient modulus used in the previous AASHTO Guide for the Design of Pavement Structures. One of the challenges of changing the material characterization is that databases, such as the Long-Term Pavement Performance Materials Database, contain older material characterization information. Thus, such databases must convert their data to the currently accepted standard (i.e., |E*|). Other investigators have presented evidence that the resilient modulus can be predicted from the dynamic modulus by using the theory of viscoelasticity. By using their prediction method, this study proposes the population of a database of measured dynamic moduli with the corresponding predicted resilient moduli to train an artificial neural network (ANN). The ANN model was verified with four 12.5-mm surface course mixtures with different aggregate types and binder types and one 25.0-mm base mixture. The dynamic moduli predicted from the measured resilient moduli with the trained ANN were found to be reasonable compared with the measured dynamic moduli. }, number={2057}, journal={TRANSPORTATION RESEARCH RECORD}, author={Lacroix, Andrew and Kim, Y. Richard and Ranjithan, S. Ranji}, year={2008}, pages={107–113} }
 @inproceedings{liu_brill, jr._mahinthakumar_ranjithan_2008, title={Contaminant Source Characterization Using Logistic Regression and Local Search Methods}, ISBN={9780784409763}, url={http://dx.doi.org/10.1061/40976(316)503}, DOI={10.1061/40976(316)503}, abstractNote={Given a set of contaminant concentration observations at sensors in a water distribution network, an inverse problem can be constructed to identify the contaminant source characteristics (including location, strength and release history) by coupling a water distribution simulation model with an optimization method. This approach, however, requires a large number of time-consuming simulation runs to evaluate potential solutions, and it may be difficult to converge on the best solution or set of possible solutions within a reasonable computational time. For this reason, it is desirable to appropriately reduce the decision space over which the optimization procedure must search to reduce the computational burden and to potentially produce faster convergence. We propose a method to reduce the decision space by efficiently identifying the probability of each point or demand node being a contaminant source location using mostly off-line computations. Then, the most likely source locations are used as a good starting point for local search methods to obtain the optimal injection profile(s) to match the observed concentration profile(s) over time. The proposed approach is demonstrated for a contamination source identification problem using an illustrative water distribution network.}, booktitle={World Environmental and Water Resources Congress 2008}, publisher={American Society of Civil Engineers}, author={Liu, Li and Brill, Jr., E. Downey and Mahinthakumar, G. and Ranjithan, S.}, year={2008}, month={May} }
 @inbook{kim_lee_ranjithan_2008, place={West Conshohocken, PA}, title={Flexible Pavement Condition Evaluation Using Deflection Basin Parameters and Dynamic Finite Element Analysis Implemented by Artificial Neural Networks}, ISBN={9780803128583}, url={http://dx.doi.org/10.1520/stp14788s}, DOI={10.1520/stp14788s}, abstractNote={This paper presents a methodology based on deflection basin parameters and artificial neural networks (ANNs) for processing dynamic falling weight deflectometer (FWD) measurements to estimate layer moduli and condition. Two-dimensional, dynamic, finite element analysis using the ABAQUS program was employed to develop the deflection information for this study. Unlike the majority of the existing backcalculation programs that iteratively adjust all the layer moduli to match the measured deflections, the proposed method first determines the subgrade modulus by means of two deflection basin parameters, Base Damage Index and Shape Factor F2, and then applies the estimated subgrade modulus and other parameters as input variables to a trained ANN to estimate the upper layers’ moduli. Procedures in predicting layer moduli for both two- and three-layer pavement systems are presented. Field FWD measurements were analyzed both by this method and by the MODULUS program, the results of which assess the capability of the proposed method. Effects of discontinuities in the asphalt layer on the resulting FWD deflections were also studied using the finite element method. It was discovered that distresses in the asphalt layer may be detected using two deflection basin parameters, Shape Factor F2 and AREA.}, booktitle={Nondestructive Testing of Pavements and Backcalculation of Moduli: Third Volume}, publisher={ASTM International}, author={Kim, YR and Lee, Y-C and Ranjithan, SR}, editor={Tayabji, S. and Lukanen, E.Editors}, year={2008}, month={Apr}, pages={514–530} }
 @inproceedings{kumar_brill_ranjithan_mahinthakumar_uber_2008, title={Source Identification for Contamination Events Involving Reactive Contaminants}, ISBN={9780784409763}, url={http://dx.doi.org/10.1061/40976(316)504}, DOI={10.1061/40976(316)504}, abstractNote={The problem of contaminant source identification in a water distribution system can be solved as an inverse problem using a simulation-optimization approach. The optimization method searches for contaminant source characteristics which lead to matching observations at the sensors. Accuracy of identification depends on the quantity and quality of data available at the sensors. The present state of the art in water quality monitoring sensors does not always allow for the detection of different kinds of contaminants in the system and they do not provide continuous contaminant concentration measurements. Some sensors provide an event detection trigger based on a specific concentration threshold yielding a binary detection/no-detection signal. Sensors also routinely monitor water quality parameters such as chlorine and pH. For example, a contaminant present in the system may react with chlorine leading to changes in chlorine-based water quality indicators. These anomalies (or deviations) in the observed water quality in the distribution system can be used as indicators of presence of contaminants in the system. A methodology for identifying the source characteristics using sensors outputting binary signals was presented by the authors recently. In the present study we investigate the interaction of reactive contaminants with chlorine in the system and its effect on water quality indicators. These anomalies indicating the presence or absence of contaminants will be used for determination of source characteristics using an evolutionary algorithm-based simulation-optimization approach.}, booktitle={World Environmental and Water Resources Congress 2008}, publisher={American Society of Civil Engineers}, author={Kumar, Jitendra and Brill, E. Downey and Ranjithan, S. Ranji and Mahinthakumar, G. and Uber, J.}, year={2008}, month={May} }
 @article{kumar_doby_baugh_brill_ranjithan_2007, title={Closure to "Optimal design of redundant water distribution networks using a cluster of workstations" by Sujay V. Kumar, Troy A. Doby, John W. Baugh Jr., E. Downey Brill, and S. Ranji Ranjithan}, volume={133}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-36349011269&partnerID=MN8TOARS}, DOI={10.1061/(asce)0733-9496(2007)133:6(580)}, number={6}, journal={Journal of Water Resources Planning and Management}, author={Kumar, S. V. and Doby, T. A. and Baugh, John and Brill, E. D. and Ranjithan, S. R.}, year={2007}, pages={580–581} }
 @inproceedings{liu_brill, jr._mahinthakumar_uber_zechman_ranjithan_2007, title={Considering Demand Variability and Measurement Uncertainties in Adaptive Source Characterization in Water Distribution Networks}, ISBN={9780784409275}, url={http://dx.doi.org/10.1061/40927(243)502}, DOI={10.1061/40927(243)502}, abstractNote={Characterizing the sources of contamination in water distribution networks continues to be a challenging problem. Several methods have been reported to address this problem. The authors previously presented and continue to investigate an adaptive search procedure that attempts to solve this problem under dynamic conditions. Since demand variability and measurement errors contribute significantly to the quality of the solutions obtained as well as the time to solve the problem, we investigate these effects on the adaptive dynamic optimization procedure. First, the effects of these variabilities and uncertainties on the solutions obtained under deterministic conditions are evaluated. Second, we incorporate them such that the search for the source characterization is conducted under noisy conditions. This paper reports the results from these investigations based on an investigation conducted for an illustrative water distribution network.}, booktitle={World Environmental and Water Resources Congress 2007}, publisher={American Society of Civil Engineers}, author={Liu, Li and Brill, Jr., E. Downey and Mahinthakumar, G. and Uber, James and Zechman, Emily M. and Ranjithan, S.}, year={2007}, month={May} }
 @inbook{sreepathi_mahinthakumar_zechman_ranjithan_brill_ma_von laszewski_2007, place={Berlin Heidelberg}, series={Lecture Notes in Computer Science}, title={Cyberinfrastructure for Contamination Source Characterization in Water Distribution Systems}, ISBN={9783540725831 9783540725848}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-540-72584-8_139}, DOI={10.1007/978-3-540-72584-8_139}, abstractNote={This paper describes a preliminary cyberinfrastructure for contaminant characterization in water distribution systems and its deployment on the grid. The cyberinfrastructure consists of the application, middleware and hardware resources. The application core consists of various optimization modules and a simulation module. This paper focuses on the development of specific middleware components of the cyberinfrastructure that enables efficient seamless execution of the application core in a grid environment. The components developed in this research include: (i) a coarse-grained parallel wrapper for the simulation module that includes additional features for persistent execution, (ii) a seamless job submission interface, and (iii) a graphical real time application monitoring tool. The performance of the cyberinfrastructure is evaluated on a local cluster and the TeraGrid.}, booktitle={Computational Science – ICCS 2007}, publisher={Springer Berlin Heidelberg}, author={Sreepathi, Sarat and Mahinthakumar, Kumar and Zechman, Emily and Ranjithan, Ranji and Brill, Downey and Ma, Xiaosong and von Laszewski, Gregor}, editor={Shi, Y. and van Albada, G.D. and Dongarra, J. and Sloot, P.M.A.Editors}, year={2007}, pages={1058–1065}, collection={Lecture Notes in Computer Science} }
 @inproceedings{kumar_zechman_brill_mahinthakumar_ranjithan_uber_2007, title={Evaluation of Non-Uniqueness in Contaminant Source Characterization Based on Sensors with Event Detection Methods}, ISBN={9780784409275}, url={http://dx.doi.org/10.1061/40927(243)513}, DOI={10.1061/40927(243)513}, abstractNote={Due to the present state of sensor technology, during a water distribution contamination event, sensors may be able to detect only the presence of a contaminant and not necessarily the complete concentration profile. Some sensors trigger a detection based on a specified threshold concentration of observation, yielding a binary detection/no-detection signal. Event detection can also be based on observed concentrations of water quality parameters, such as pH and chlorine, which are routinely measured. These concentration observations are then processed through event detection algorithms to yield a detection/no-detection signal. These event detection techniques filter the measured concentrations at sensors to produce a discrete signal. When using this filtered information to characterize the contamination source, the certainty of identifying a unique solution is likely reduced, i.e., a set of widely different source characteristics may provide a match for the sensor observations. The authors previously presented an evolutionary algorithm-based procedure for source characterization and for assessing nonuniqueness by generating a set of maximally different alternatives. The procedure is extended here to characterize a contaminant source and any non-uniqueness arising by using sensor information processed through different event detection methods.}, booktitle={World Environmental and Water Resources Congress 2007}, publisher={American Society of Civil Engineers}, author={Kumar, Jitendra and Zechman, E. M. and Brill, E. D. and Mahinthakumar, G. and Ranjithan, S. and Uber, J.}, year={2007}, month={May} }
 @inproceedings{mirghani_tryby_ranjithan_mahinthakumar_2007, title={Evolutionary Algorithms-Based Grid Computational Framework for Solving Groundwater Characterization Problems}, ISBN={9780784409275}, url={http://dx.doi.org/10.1061/40927(243)160}, DOI={10.1061/40927(243)160}, abstractNote={This paper investigates groundwater system characterization problem, in this inverse problem the contaminant signals at monitoring wells are recorded to recreate the pollution profiles. In this study, simulation-optimization approach is a technique utilized to solve inverse problems by formulating them as an optimization model, where evolutionary algorithms are used to perform the search. In this approach, the PDE groundwater transport simulation model (simulation model) is solved iteratively during the evolutionary search, which in general can be computationally expensive. To overcome this limitation, high performance computing and grid computing are used to improve the simulation model efficiency. The parallel PDE groundwater transport simulation model is then coupled with an evolutionary computation search procedure to solve two instances of groundwater inverse problems. The results demonstrate the performance of the grid-enabled simulation-optimization approach in terms of solution quality and computational performance.}, booktitle={World Environmental and Water Resources Congress 2007}, publisher={American Society of Civil Engineers}, author={Mirghani, Baha and Tryby, Michael and Ranjithan, Ranji and Mahinthakumar, Kumar}, year={2007}, month={May} }
 @article{zechman_ranji ranjithan_2007, title={Evolutionary computation-based approach for model error correction and calibration}, volume={30}, ISSN={0309-1708}, url={http://dx.doi.org/10.1016/j.advwatres.2006.11.013}, DOI={10.1016/j.advwatres.2006.11.013}, abstractNote={Calibration is typically used for improving the predictability of mechanistic simulation models by adjusting a set of model parameters and fitting model predictions to observations. Calibration does not, however, account for or correct potential misspecifications in the model structure, limiting the accuracy of modeled predictions. This paper presents a new approach that addresses both parameter error and model structural error to improve the predictive capabilities of a model. The new approach simultaneously conducts a numeric search for model parameter estimation and a symbolic (regression) search to determine a function to correct misspecifications in model equations. It is based on an evolutionary computation approach that integrates genetic algorithm and genetic programming operators. While this new approach is designed generically and can be applied to a broad array of mechanistic models, it is demonstrated for an illustrative case study involving water quality modeling and prediction. Results based on extensive testing and evaluation, show that the new procedure performs consistently well in fitting a set of training data as well as predicting a set of validation data, and outperforms a calibration procedure and an empirical model fitting procedure.}, number={5}, journal={Advances in Water Resources}, publisher={Elsevier BV}, author={Zechman, Emily M. and Ranji Ranjithan, S.}, year={2007}, month={May}, pages={1360–1370} }
 @article{zechman_ranjithan_2007, title={Generating Alternatives Using Evolutionary Algorithms for Water Resources and Environmental Management Problems}, volume={133}, ISSN={0733-9496 1943-5452}, url={http://dx.doi.org/10.1061/(asce)0733-9496(2007)133:2(156)}, DOI={10.1061/(ASCE)0733-9496(2007)133:2(156)}, abstractNote={Contemporary heuristic search procedures [e.g., evolutionary algorithms (EAs)] continue to offer increased capabilities for systematic search for a range of water resources and environmental management problems. These problems are often riddled, however, with numerous unquantifiable issues that are important when making decisions, but escape being incorporated in the system model. The mathematically optimal solution to such an incompletely defined model may be found unrealistic or altogether incorrect for the real problem. Optimization procedures could still be made useful if they can be utilized effectively to generate, in addition to the optimal solution, a small number of different alternatives that are near optimal. Alternatives with maximal differences in the decision variable values are expected to perform differently with respect to the unmodeled issues, providing valuable choices when making decisions. Although successful alternative generation procedures have been reported for mathematical progra...}, number={2}, journal={Journal of Water Resources Planning and Management}, publisher={American Society of Civil Engineers (ASCE)}, author={Zechman, Emily M. and Ranjithan, Ranji S.}, year={2007}, month={Mar}, pages={156–165} }
 @inproceedings{tryby_propato_ranjithan_2007, title={Monitoring Sensor Network Design for Water Distribution Source Inversion Problems}, ISBN={9780784409275}, url={http://dx.doi.org/10.1061/40927(243)521}, DOI={10.1061/40927(243)521}, abstractNote={Monitoring network design for the detection of contaminants in water distribution systems is currently an active area of research. Much of the effort has been directed at the contamination detection sub-problem and the expression of public health protection objectives. Monitoring networks, once they are in place, however, are likely to be used to gather monitoring data for source inversion as well. Thus, the design of these networks with the unique objectives associated with source inversion in mind is a necessity. Source inversion problems in water distribution systems are inherently under-determined and exhibit solution non-uniqueness. Judicious monitoring design is one approach for addressing these difficulties. Herein, discrete linear inverse theory is applied to the monitoring sensor network design problem; in particular, a quantitative description of solution existence, uniqueness, stability, and resolution is developed using singular value decomposition.}, booktitle={World Environmental and Water Resources Congress 2007}, publisher={American Society of Civil Engineers}, author={Tryby, M. E. and Propato, M. and Ranjithan, R.}, year={2007}, month={May} }
 @article{raghavachar_mahinthakumar_worley_zechman_ranjithan_2007, title={Parallel Performance Modeling using a Genetic Programming-based Error Correction Procedure}, volume={83}, ISSN={0037-5497 1741-3133}, url={http://dx.doi.org/10.1177/0037549707084691}, DOI={10.1177/0037549707084691}, abstractNote={ Performance models of high performance computing (HPC) applications are important for several reasons. First, they provide insight to designers of HPC systems on the role of subsystems such as the processor or the network in determining application performance. Second, they allow HPC centers more accurately to target procurements to resource requirements. Third, they can be used to identify application performance bottlenecks and to provide insights about scalability issues. The suitability of a performance model, however, for a particular performance investigation is a function of both the accuracy and the cost of the model. }, number={7}, journal={SIMULATION}, publisher={SAGE Publications}, author={Raghavachar, Kavitha and Mahinthakumar, G. and Worley, Patrick and Zechman, Emily and Ranjithan, Ranji}, year={2007}, month={Jul}, pages={515–527} }
 @inbook{mahinthakumar_von laszewski_ranjithan_brill_uber_harrison_sreepathi_zechman_2006, place={Berlin Heidelberg}, series={Lecture Notes in Computer Science}, title={An Adaptive Cyberinfrastructure for Threat Management in Urban Water Distribution Systems}, volume={3993}, ISBN={9783540343837 9783540343844}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/11758532_54}, DOI={10.1007/11758532_54}, abstractNote={Threat management in drinking water distribution systems involves real-time characterization of any contaminant source and plume, design of control strategies, and design of incremental data sampling schedules. This requires dynamic integration of time-varying measurements along with analytical modules that include simulation models, adaptive sampling procedures, and optimization methods. These modules are compute-intensive, requiring multi-level parallel processing via computer clusters. Since real-time responses are critical, the computational needs must also be adaptively matched with available resources. This requires a software system to facilitate this integration via a high-performance computing architecture such that the measurement system, the analytical modules and the computing resources can mutually adapt and steer each other. This paper describes the development of such an adaptive cyberinfrastructure system facilitated by a dynamic workflow design.}, booktitle={Computational Science – ICCS 2006}, publisher={Springer}, author={Mahinthakumar, Kumar and von Laszewski, Gregor and Ranjithan, Ranji and Brill, Downey and Uber, Jim and Harrison, Ken and Sreepathi, Sarat and Zechman, Emily}, editor={Alexandrov, V.N. and Albada, G.D. and Sloot, P.M.A. and Dongarra, J.Editors}, year={2006}, pages={401–408}, collection={Lecture Notes in Computer Science} }
 @article{fu_brill_ranjithan_2006, title={Conjunctive use of models to design cost-effective ozone control strategies}, volume={56}, ISSN={["2162-2906"]}, DOI={10.1080/10473289.2006.10464492}, abstractNote={Abstract The management of tropospheric ozone (O3) is particularly difficult. The formulation of emission control strategies requires considerable information including: (1) emission inventories, (2) available control technologies, (3) meteorological data for critical design episodes, and (4) computer models that simulate atmospheric transport and chemistry. The simultaneous consideration of this information during control strategy design can be exceedingly difficult for a decision-maker. Traditional management approaches do not explicitly address cost minimization. This study presents a new approach for designing air quality management strategies; a simple air quality model is used conjunctively with a complex air quality model to obtain low-cost management strategies. A simple air quality model is used to identify potentially good solutions, and two heuristic methods are used to identify cost-effective control strategies using only a small number of simple air quality model simulations. Subsequently, the resulting strategies are verified and refined using a complex air quality model. The use of this approach may greatly reduce the number of complex air quality model runs that are required. An important component of this heuristic design framework is the use of the simple air quality model as a screening and exploratory tool. To achieve similar results with the simple and complex air quality models, it may be necessary to “tweak” or calibrate the simple model. A genetic algorithm-based optimization procedure is used to automate this tweaking process. These methods are demonstrated to be computationally practical using two realistic case studies, which are based on data from a metropolitan region in the United States.}, number={6}, journal={JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION}, author={Fu, Joshua S. and Brill, E. Downey, III and Ranjithan, S. Ranji}, year={2006}, month={Jun}, pages={800–809} }
 @article{kumar_doby_baugh_brill_ranjithan_2006, title={Optimal design of redundant water distribution networks using a cluster of workstations}, volume={132}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-33747304468&partnerID=MN8TOARS}, DOI={10.1061/(ASCE)0733-9496(2006)132:5(374)}, abstractNote={A genetic algorithm (GA)-based method for the least-cost design of looped pipe networks for various levels of redundancy is presented in this paper. Redundancy constraints are introduced in the optimization model by considering the number of pipes assumed to be out of service at any one time. Using this approach, trade-off relationships between cost and redundancy are developed. The GA-based approach is computationally intensive, and implementations on a custom fault-tolerant distributed computing framework, called Vitri, are used to satisfy the computational requirements. The design methodology is applied to two water distribution networks of different sizes, and a comparison of the performance of the distributed GAs for the design problems is also presented. We conclude that a GA-based approach to obtaining cost-effective, redundant solutions for the least-cost design of looped pipe networks can be effectively used on a heterogeneous network of nondedicated workstations.}, number={5}, journal={Journal of Water Resources Planning and Management}, author={Kumar, S. V. and Doby, T. A. and Baugh, John and Brill, E. D. and Ranjithan, S. R.}, year={2006}, pages={374–384} }
 @inproceedings{zechman_mirghani_mahinthakumar_ranjithan_2005, title={A Genetic Programming-Based Surrogate Model Development and Its Application to a Groundwater Source Identification Problem}, ISBN={9780784407929}, url={http://dx.doi.org/10.1061/40792(173)341}, DOI={10.1061/40792(173)341}, abstractNote={This paper investigates a groundwater source identification problem in which chemical signals at observation wells are used to reconstruct the pollution loading scenario. This inverse problem is solved using a simulation-optimization approach that uses a genetic algorithm to conduct the search. As the numerical pollution-transport model is solved iteratively during the heuristic search, the evolutionary search can be in general computationally intensive. This is addressed by constructing a surrogate modeling approach that is able to predict quickly the concentration profiles at the observation wells. A genetic program is used in the development of the surrogate models that provides an acceptable prediction performance. The surrogate model, which replaces the numerical simulation model, is then coupled with the evolutionary search procedure to solve the inverse problem. The results will illustrate 1) the performance of the surrogate model in predicting the concentration compared with the predictions using the original numerical model, and 2) the quality of the solution to the inverse problem obtained using the surrogate model to that obtained using the numerical model.}, booktitle={Impacts of Global Climate Change}, publisher={American Society of Civil Engineers}, author={Zechman, Emily and Mirghani, Baha and Mahinthakumar, G. and Ranjithan, S. Ranji}, year={2005}, month={Jul} }
 @inproceedings{zechman_mahinthakumar_ranjithan_2005, title={Investigation and Demonstration of an Evolutionary Computation-Based Model Correction Procedure for a Numerical Simulation Model}, ISBN={9780784407929}, url={http://dx.doi.org/10.1061/40792(173)346}, DOI={10.1061/40792(173)346}, abstractNote={Traditional model calibration attempts to correct a model so that the model output will match a set of system observations by tweaking a set of model parameters. Potential model structural error limits, however, the effectiveness and accuracy of calibration, undermining the predictive capabilities of the calibrated model. An evolutionary computation-based model error correction procedure that couples an evolutionary algorithm and a genetic program was previously developed and tested for two analytical models. Due to nonuniqueness in the solution space, numerous forms of correction terms that similarly fit the observation data were found. This procedure is further investigated to explore and identify alternative correction terms that not only provide a good fit but also results in good prediction performance. This approach is then demonstrated using a numerical groundwater contaminant transport simulation model.}, booktitle={Impacts of Global Climate Change}, publisher={American Society of Civil Engineers}, author={Zechman, Emily and Mahinthakumar, G. and Ranjithan, S. Ranji}, year={2005}, month={Jul} }
 @inproceedings{zechman_ranjithan_2005, place={New York}, title={Multipopulation cooperative coevolutionary programming (MCCP) to enhance design innovation}, ISBN={1595930108}, url={http://dx.doi.org/10.1145/1068009.1068286}, DOI={10.1145/1068009.1068286}, abstractNote={This paper describes the development of an evolutionary algorithm called Multipopulation Cooperative Coevolutionary Programming (MCCP) that extends Genetic Programming (GP) to search for a set of maximally different solutions for program induction problems. The GP search is structured to generate a set of alternatives that are similar in design performance, but are dissimilar from each other in the solution (or design parameter) space. This is expected to yield potentially more creative designs, thus enhancing design innovation. Application of MCCP is demonstrated through an illustrative example involving GP-based classification of genetic data to diagnose malignancy in cancer. Four different classifiers, based on highly dissimilar combinations of genes, but with similar prediction performances were generated. As these classifiers use a diverse set of genes, they are collectively more effective in screening cancer samples that may not all properly express every gene.}, booktitle={Proceedings of the 2005 conference on Genetic and evolutionary computation  - GECCO '05}, publisher={ACM Press}, author={Zechman, Emily M. and Ranjithan, S. Ranji}, editor={Beyer, H-GEditor}, year={2005} }
 @article{ranjithan_2005, title={Role of evolutionary computation in environmental and water resources systems analysis}, volume={131}, DOI={10.1061/(asce)0733-9496(2005)131:1(1)}, abstractNote={Since the 1960s, systems analytic methods have played a key role in environmental and water resources planning and management. An array of systematic search procedures as well as statistical methods continues to be the focus of investigation in environmental and water resources systems (EWRS). With the advent of modern and faster computing resources at a high degree of affordability, the system simulation models are now able to incorporate more processes and their interactions, resulting in relatively more complex model structures. Therefore, the coupling of simulation models with search methods for optimization have become increasingly challenging. In many cases, complexities that arise from, for example, nonlinearity, discontinuity, and discreteness in modern simulation models, limit the application of traditional search methods, e.g., mathematical programming procedures. Such limitations have been overcome recently by directly coupling the simulation models with heuristic search procedures. While this directly coupled simulation–optimization (S–O) approach is, in general, computationally demanding, it has proven to be a viable approach given cheaper and faster computational resources. Among the array of modern heuristic approaches (e.g., simulated annealing, tabu search, genetic algorithms, evolutionary strategies, particle swarm method, and ant colony optimization) that support an S–O framework for EWRS problems, the collection of methods—namely, genetic algorithms, evolutionary strategies, genetic programming, and evolutionary programming— encompassed within the broad category of evolutionary computation (EC) offers a multitude of capabilities. Starting in the early 1990s, evolutionary algorithms (including genetic algorithms and evolutionary strategies) have been applied and demonstrated for system optimization in the context of EWRS problems. Numerous studies over the past decade in areas that include groundwater monitoring and remediation design, water distribution network design, reservoir optimization, watershed management, and air pollution control show not only the viability of applying evolutionary algorithms to these challenging problems, but also the added benefits of using a directly coupled S–O approach enabled by these techniques. This special issue also represents a cross section of recent investigations related to EC methods and their applications in EWRS. In addition to system optimization, many EWRS problems pose several interesting scenarios that call for additional systems analytic capabilities. For example, most EWRS problems consider multiple competing objectives, requiring the systems analytic methods to provide multiobjective optimization capabilities. The structure of EC methods readily support efficient search for Pareto optimal solutions to a multiobjective optimization problem. This is currently an active area of research in the EC re-}, number={1}, journal={Journal of Water Resources Planning and Management}, author={Ranjithan, S. R.}, year={2005}, pages={02-} }
 @article{kaplan_barlaz_ranjithan_2004, title={A Procedure for Life-Cycle-Based Solid Waste Management with Consideration of Uncertainty}, volume={8}, ISSN={1088-1980}, url={http://dx.doi.org/10.1162/1088198043630531}, DOI={10.1162/1088198043630531}, abstractNote={The development of integrated solid‐waste management (SWM) strategies that are efficient with respect to both cost and environmental performance is a complex task. It must incorporate the numerous interrelations among different unit operations in the solid waste system (e.g., collection, recycling, and combustion), and the large number of design parameters that affect estimates of cost and environmental emissions. Uncertainty in design and operational parameters can lead to uncertainty in the estimates of cost and emissions. This article describes an extension of the capability of the Integrated Solid Waste Management Decision Support Tool (ISWM DST) to enable consideration of the effects of uncertainty in input parameters. The uncertainty analysis capability is illustrated using a hypothetical case study of a typical municipality. Results show that increased expenditure does not necessarily result in a reduction in the expected levels of environmental emissions and that some SWM alternatives may be more robust, although deterministic estimates of their expected performances are similar. The uncertainty analysis also facilitates use of the ISWM DST by policy makers responsible for evaluation of the expected effect of SWM practices on, for example, greenhouse‐gas emissions.}, number={4}, journal={Journal of Industrial Ecology}, publisher={Wiley}, author={Kaplan, P. Özge and Barlaz, Morton A. and Ranjithan, S. Ranji}, year={2004}, month={Sep}, pages={155–172} }
 @article{zechman_ranjithan_2004, title={An evolutionary algorithm to generate alternatives (EAGA) for engineering optimization problems}, volume={36}, ISSN={0305-215X 1029-0273}, url={http://dx.doi.org/10.1080/03052150410001704863}, DOI={10.1080/03052150410001704863}, abstractNote={Typically for a real optimization problem, the optimal solution to a mathematical model of that real problem may not always be the ‘best’ solution when considering unmodeled or unquantified objectives during decision-making. Formal approaches to explore efficiently for good but maximally different alternative solutions have been established in the operations research literature, and have been shown to be valuable in identifying solutions that perform expectedly well with respect to modeled and unmodeled objectives. While the use of evolutionary algorithms (EAs) to solve real engineering optimization problems is becoming increasingly common, systematic alternatives-generation capabilities are not fully extended for EAs. This paper presents a new EA-based approach to generate alternatives (EAGA), and illustrates its applicability via two test problems. A realistic airline route network design problem was also solved and analyzed successfully using EAGA. The EAGA promises to be a flexible procedure for exploring alternative solutions that could assist when making decisions for real engineering optimization problems riddled with unmodeled or unquantified issues.}, number={5}, journal={Engineering Optimization}, publisher={Informa UK Limited}, author={Zechman, Emily M. and Ranjithan, S. Ranji}, year={2004}, month={Oct}, pages={539–553} }
 @inproceedings{dorn_ranjithan_2004, title={Generating Urban Watershed Management Alternatives Using Evolutionary Algorithms}, ISBN={9780784407370}, url={http://dx.doi.org/10.1061/40737(2004)241}, DOI={10.1061/40737(2004)241}, abstractNote={The design of urban drainage networks is complicated by the need to consider a number of issues that conflict and compete with the goal of managing flood impacts. These issues primarily include environmental considerations, but may also include issues such as developable land impacts, system reliability, wetland impacts, aesthetics, etc., some of which may not be modeled explicitly. Modeling to generate alternatives (MGA) is a formal optimization-based technique to find near optimal alternatives that are maximally different from one another with respect to their decision attributes. This paper presents a new evolutionary algorithm (EA)-based technique, the Solution Set Algorithm (SSA), for performing MGA and its application to design problem involving the design a least-cost drainage network using the EPA's Storm Water Management Model (SWMM).}, booktitle={Critical Transitions in Water and Environmental Resources Management}, publisher={American Society of Civil Engineers}, author={Dorn, J. L. and Ranjithan, S.}, year={2004}, month={Jun} }
 @inbook{zechman_ranjithan_2003, title={Are the "best" solutions to a real optimization problem always found in the noninferior set? Evolutionary algorithm for generating alternatives (EAGA)}, volume={2724}, ISBN={3540406026}, DOI={10.1007/3-540-45110-2_55}, abstractNote={Evolutionary algorithms (EAs) continue to offer an effective, powerful, and sometimes exclusive way to search for solutions to real optimization problems. While these algorithms can help solve a complex optimization problem, whether the results represent the “best” choices for making decisions about a solution to a real problem is questionable. In decision-making problems that are ill posed, all objectives may not be defined clearly and therefore not quantitatively captured in the optimization model [1]. The noninferior set of solutions to the optimization model being solved may not necessarily contain the best solution to the actual problem.}, booktitle={Genetic and evolutionary computation--GECCO 2003: Genetic and Evolutionary Computation Conference, Chicago, IL, USA, July 12-16, 2003: Proceedings}, publisher={Berlin; New York: Springer}, author={Zechman, E. M. and Ranjithan, S. R.}, year={2003}, pages={1622–1623} }
 @article{barlaz_kaplan_ranjithan_rynk_2003, title={Comparing recycling, composting and landfills}, volume={44}, number={9}, journal={BioCycle}, author={Barlaz, M. A. and Kaplan, P. O. and Ranjithan, S. R. and Rynk, R.}, year={2003}, pages={60-} }
 @article{harrell_ranjithan_2003, title={Detention pond design and land use planning for watershed management}, volume={129}, DOI={10.1061/(ASCE)0733-9486(2003)129:2(98)}, number={2}, journal={Journal of Water Resources Planning and Management}, author={Harrell, L. J. and Ranjithan, S. R.}, year={2003}, pages={98–106} }
 @article{barlaz_kaplan_ranjithan_rynk_2003, title={Evaluating environmental impacts of solid waste management alternatives}, volume={44}, number={10}, journal={BioCycle}, author={Barlaz, M. A. and Kaplan, P. O. and Ranjithan, S. R. and Rynk, R.}, year={2003}, pages={52–56} }
 @inbook{dorn_ranjithan_2003, title={Evolutionary multiobjective optimization in watershed water quality management}, volume={2632}, ISBN={3540018697}, DOI={10.1007/3-540-36970-8_49}, abstractNote={The watershed water quality management problem considered in this study involves the identification of pollution control choices that help meet water quality targets while sustaining necessary growth. The primary challenge is to identify nondominated management choices that represent the noninferior tradeoff between the two competing management objectives, namely allowable urban growth and water quality. Given the complex simulation models and the decision space associated with this problem, a genetic algorithm-based multiobjective optimization (MO) approach is needed to solve and analyze it. This paper describes the application of the Nondominated Sorting Algorithm II (NSGA-II) to this realistic problem. The effects of different population sizes and sensitivity to random seed are explored. As the water quality simulation run times can become prohibitive, appropriate stopping criteria to minimize the number of fitness evaluations are being investigated. To compare with the NSGA-II results, the MO watershed management problem was also analyzed via an iterative application of a hybrid GA/local-search method that solved a series of single objective ε-constraint formilations of the multiobjective problem. In this approach, the GA solutions were used as the starting points for the Nelder-Mead local search algorithm. The results indicate that NSGA-II offers a promising approach to solving this complex, real-world MO watershed management problem.}, booktitle={Evolutionary multi-criterion optimization: Second international conference, EMO 2003, Faro, Portugal, April 8-11, 2003: proceedings}, publisher={Berlin; New York: Springer}, author={Dorn, J. L. and Ranjithan, S. R.}, year={2003}, pages={692–706} }
 @inbook{xu_ranjithan_kim_2003, title={Using the asphalt pavement layer condition assessment program - Case studies}, ISBN={0309085977}, DOI={10.3141/1860-08}, abstractNote={ The Asphalt Pavement Layer Condition Assessment Program (APLCAP) is developed in this research to help highway agencies assess layer conditions of asphalt pavements. APLCAP implements a new integrated procedure for condition assessment from falling-weight deflectometer (FWD) deflections. The main components of this procedure include screening of FWD raw deflections, predictions of condition indicators from FWD measurements, structural adjustments for the predicted condition indicators, and layer condition evaluation based on the adjusted condition indicators. This procedure was developed on the basis of dynamic nonlinear finite element analysis and calibrated using field measurements. The three case studies presented show that the APLCAP algorithms can predict the asphalt concrete modulus, pavement critical strains, and strengths of the base and subgrade quite well, but not the compressive strain in the aggregate base layer. Although the APLCAP procedure includes the complicated dynamic effect of FWD loading and nonlinear behavior of unbound materials, the time to obtain results from this procedure is insignificant and therefore suitable for real-time evaluation of pavement conditions. }, number={1860}, booktitle={Pavement assessment, monitoring and evaluation 2003}, publisher={Washington, DC: Transportation Research Board}, author={Xu, B. and Ranjithan, S. R. and Kim, Y. R.}, year={2003}, pages={66–75} }
 @inproceedings{kumar_ranjithan_2002, place={New York}, title={Evaluation of the Constraint Method-Based Multiobjective Evolutionary Algorithm (CMEA) for a Three-Objective Optimization Problem}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2002}, publisher={Morgan Kaufmann}, author={Kumar, S.V. and Ranjithan, S.}, editor={Langdon, WB and Cantu-Paz, E and Mathias, K and Roy, R and Davis, D and Poli, R and Balakrishnan, K and Honavar, V and Rudolph, G and Wegener, J and et al.Editors}, year={2002}, month={Jul}, pages={431–438} }
 @article{solano_dumas_harrison_ranjithan_barlaz_downey brill_2002, title={Life-Cycle-based Solid Waste Management. II: Illustrative Applications}, volume={128}, ISSN={0733-9372 1943-7870}, url={http://dx.doi.org/10.1061/(asce)0733-9372(2002)128:10(993)}, DOI={10.1061/(asce)0733-9372(2002)128:10(993)}, abstractNote={A companion paper described the development of the integrated solid waste management (ISWM) model that considers cost, energy, and environmental releases associated with management of municipal solid waste. This paper demonstrates the application of the ISWM model to a hypothetical, but realistic, case study. Several solid waste management (SWM) scenarios are studied, including the variation in energy and environmental emissions among alternate SWM strategies; the effect of mandated waste diversion (through recycling and other beneficial uses of waste such as combustion to recover energy) on environmental releases and cost; the tradeoff between cost and the level of waste diversion; and the tradeoff between cost and greenhouse gas emissions. In addition, the flexibility of the model is illustrated by the identification of alternate SWM strategies that meet approximately the same objectives using distinctly different combinations of unit processes. This flexibility may be of importance to local solid waste management planners who must implement new SWM programs. Use of the model illustrates the potential impact of solid waste management policies and regulations on global environmental emissions.}, number={10}, journal={Journal of Environmental Engineering}, publisher={American Society of Civil Engineers (ASCE)}, author={Solano, Eric and Dumas, Robert D. and Harrison, Kenneth W. and Ranjithan, S. Ranji and Barlaz, Morton A. and Downey Brill, E.}, year={2002}, month={Oct}, pages={993–1005} }
 @article{solano_ranjithan_barlaz_brill_2002, title={Life-cycle-based solid waste management. I: Model development}, volume={128}, DOI={10.1061/(asce)0733-9372(2002)128:10(981)}, abstractNote={This paper describes an integrated solid waste management (ISWM) model to assist in identifying alternative SWM strategies that meet cost, energy, and environmental emissions objectives. An SWM system consisting of over 40 unit processes for collection, transfer, separation, treatment (e.g., combustion, composting), and disposal of waste as well as remanufacturing facilities for processing recycled material is defined. Waste is categorized into 48 items and their generation rates are defined for three types of sectors: single-family dwelling, multifamily dwelling, and commercial. The mass flow of each item through all possible combinations of unit processes is represented in a linear programming model using a unique modeling approach. Cost, energy consumption, and environmental emissions associated with waste processing at each unit process are computed in a set of specially implemented unit process models. A life-cycle approach is used to compute energy consumption and emissions of CO, fossil- and biomas...}, number={10}, journal={Journal of Environmental Engineering (New York, N.Y.)}, author={Solano, E. and Ranjithan, S. R. and Barlaz, Morton and Brill, E. D.}, year={2002}, pages={981–992} }
 @inbook{xu_ranjithan_kim_2002, title={New condition assessment procedure for asphalt pavement layers using failing weight deflectometer deflections}, ISBN={030907732X}, DOI={10.3141/1806-07}, abstractNote={ Nondestructive condition assessment criteria were developed for application in conjunction with the condition evaluation indicators that are estimated based on falling weight deflectometer (FWD) deflections. Data obtained from state departments of transportation and DATAPAVE 2.0 were used in developing these criteria. To account for the effects of pavement structure and temperature on FWD deflection analysis, structure and temperature correction procedures based on synthetic databases were applied. Also, a deflection prescreening procedure was established to identify and correct any irregular deflection basins potentially arising from measurement errors. All the calibrated predictive procedures, structure and temperature correction procedures, and prescreening algorithms were incorporated into the user-friendly deflection analysis program with graphical interface, Asphalt Pavement Layer Condition Assessment Program, or APLCAP. }, number={1806}, booktitle={Assessing and evaluating pavements, 2002}, publisher={Washington, D.C.: National Academy Press}, author={Xu, B. and Ranjithan, S. R. and Kim, Y. R.}, year={2002}, pages={57–69} }
 @inbook{xu_ranjithan_kim_2002, title={New relationships between failing weight deflectometer deflections and asphalt pavement layer condition indicators}, ISBN={030907732X}, DOI={10.3141/1806-06}, abstractNote={ New relationships have been identified between the layer condition indicators of flexible pavements and falling weight deflectometer (FWD) deflections. Synthetic databases were generated using dynamic finite element analysis with nonlinear material models. The sensitivity of various deflection basin parameters (DBPs) to layer conditions was comprehensively examined on the basis of the developed databases. Three types of layer condition indicators were identified in the study, including DBPs, effective layer moduli, and stresses and strains. The DBPs identified from the sensitivity study were used in developing new relationships between the selected condition indicators and FWD deflections by applying regression and artificial neural network techniques. Even though these relationships include the complicated dynamic effect of FWD loading and nonlinear behavior of unbound materials, the time to obtain results from these procedures is insignificant, thus making the procedures suitable for field implementation. }, number={1806}, booktitle={Assessing and evaluating pavements, 2002}, publisher={Washington, D.C.: National Academy Press}, author={Xu, B. and Ranjithan, S. R. and Kim, Y. R.}, year={2002}, pages={48–56} }
 @inbook{ranjithan_chetan_dakshina_2001, title={Constraint method-based evolutionary algorithm (CMEA) for multiobjective optimization}, volume={1993}, ISBN={3540417451}, DOI={10.1007/3-540-44719-9_21}, abstractNote={Evolutionary algorithms are becoming increasingly valuable in solving large-scale, realistic engineering multiobjective optimization (MO) problems, which typically require consideration of conflicting and competing design issues. The new procedure, Constraint Method-Based Evolutionary Algorithm (CMEA), presented in this paper is based upon underlying concepts in the constraint method described in the mathematical programming literature. Pareto optimality is achieved implicitly via a constraint approach, and convergence is enhanced by using beneficial seeding of the initial population. CMEA is evaluated by solving two test problems reported in the multiobjective evolutionary algorithm (MOEA) literature. Performance comparisons based on quantitative metrics for accuracy, coverage, and spread are presented. CMEA is relatively simple to implement and incorporate into existing implementations of evolutionary algorithm-based optimization procedures.}, booktitle={Evolutionary multi-criterion optimization: First international conference, EMO 2001, Zurich, Switzerland, March 7-9, 2001: Proceedings}, publisher={Berlin; New York: Springer}, author={Ranjithan, S. R. and Chetan, S. K. and Dakshina, H. K.}, year={2001}, pages={299–313} }
 @article{harrison_dumas_solano_barlaz_brill_ranjithan_2001, title={Decision support tool for life-cycle-based solid waste management}, volume={15}, DOI={10.1061/(ASCE)0887-3801(2001)15:1(44)}, abstractNote={Existing solid waste management (SWM) planning software provides only limited assistance to decision makers struggling to find strategies that address their multifarious concerns. The combinatorial nature (many waste items and many management options) and multiple objectives of the SWM problem severely constrain the effectiveness of a manual search process using these tools. Recognizing this, researchers have proposed several optimization-based search procedures. These methods, however, enjoy limited use due to the substantial expertise required for their application. This paper presents a new computer-based decision support framework that addresses these limitations. The new framework integrates process models that quantify the life-cycle inventory of a range of pollutants and costs for an extensive municipal solid waste system, an optimization search procedure that identifies strategies that meet cost and environmental objectives and site-specific restrictions, and a user-friendly interface that facilitates utilization of these components by practitioners. After describing the software design, the use and value of the tool in typical waste management scenarios is demonstrated through a hypothetical, but realistic, case study in which several alternative SWM strategies are generated and examined.}, number={1}, journal={Journal of Computing in Civil Engineering}, author={Harrison, K. W. and Dumas, R. D. and Solano, E. and Barlaz, Morton and Brill, E. D. and Ranjithan, S. R.}, year={2001}, pages={44–58} }
 @article{loughlin_ranjithan_brill_baugh_2001, title={Genetic algorithm approaches for addressing unmodeled objectives in optimization problems}, volume={33}, ISSN={["0305-215X"]}, DOI={10.1080/03052150108940933}, abstractNote={Abstract Public sector decision-making typically involves complex problems that are often not completely understood. In these problems, there are invariably unmodeled issues that can greatly impact the acceptability of solutions. Modeling to Generate Alternatives (MGA) is an approach for addressing unmodeled issues in an optimization context. MGA techniques are used to generate a small number of good, yet very different, solutions to optimization problems. Because these solutions are different in decision space, they may differ considerably in performance when unmodeled objectives are considered. Many problems are sufficiently complex that traditional optimization solution procedures, and therefore traditional MGA techniques, are not readily applicable. Two techniques for performing MGA using genetic algorithms (GAs) are investigated and compared. One of these techniques, which uses specialized MGA operators, is shown to produce solutions that are both better in quality and more different. This technique is also demonstrated for a realistic air quality management problem.}, number={5}, journal={ENGINEERING OPTIMIZATION}, author={Loughlin, DH and Ranjithan, SR and Brill, ED and Baugh, JW}, year={2001}, pages={549–569} }
 @article{loughlin_ranjithan_baugh_brill_2000, title={Application of genetic algorithms for the design of ozone control strategies}, volume={50}, ISSN={["2162-2906"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-0034195231&partnerID=MN8TOARS}, DOI={10.1080/10473289.2000.10464133}, abstractNote={ABSTRACT Designing air quality management strategies is complicated by the difficulty in simultaneously considering large amounts of relevant data, sophisticated air quality models, competing design objectives, and unquantifiable issues. For many problems, mathematical optimization can be used to simplify the design process by identifying cost-effective solutions. Optimization applications for controlling nonlinearly reactive pollutants such as tropospheric ozone, however, have been lacking because of the difficulty in representing nonlinear chemistry in mathematical programming models. We discuss the use of genetic algorithms (GAs) as an alternative optimization approach for developing ozone control strategies. A GA formulation is described and demonstrated for an urban-scale ozone control problem in which controls are considered for thousands of pollutant sources simultaneously. A simple air quality model is integrated into the GA to represent ozone transport and chemistry. Variations of the GA formulation for multiobjective and chance-constrained optimization are also presented. The paper concludes with a discussion of the practicality of using more sophisticated, regulatory-scale air quality models with the GA. We anticipate that such an approach will be practical in the near term for supporting regulatory decision-making.}, number={6}, journal={JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION}, author={Loughlin, DH and Ranjithan, SR and Baugh, JW and Brill, ED}, year={2000}, month={Jun}, pages={1050–1063} }
 @inproceedings{loughlin_ranjithan_1999, place={San Francisco, CA}, title={Chance-Constrained Genetic Algorithms}, volume={1}, booktitle={GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Morgan Kaufmann}, author={Loughlin, D.H. and Ranjithan, S.}, editor={Banzhaf, W and Daida, J and Eiben, AE and Garzon, MH and Honavar, VEditors}, year={1999}, month={Jul}, pages={369–376} }
 @inproceedings{harrell_ranjithan_1999, place={San Francisco, CA}, title={Evaluation of Alternative Penalty Function Implementations in a Watershed Management Design Problem}, volume={2}, ISBN={978-1-55860-611-1}, booktitle={GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference}, publisher={Morgan Kaufmann}, author={Harrell, L.J. and Ranjithan, S.}, editor={Banzhaf, W and Daida, J and Eiben, AE and Garzon, M and Honavar, VEditors}, year={1999}, month={Jul}, pages={1551–1558} }
 @article{weitz_barlaz_ranjithan_brill_thorneloe_ham_1999, title={Life Cycle Management of Municipal Solid Waste}, volume={4}, ISSN={0948-3349 1614-7502}, url={http://dx.doi.org/10.1007/bf02979496}, DOI={10.1007/bf02979496}, number={4}, journal={The International Journal of Life Cycle Assessment}, publisher={Springer Science and Business Media LLC}, author={Weitz, Keith and Barlaz, Morton and Ranjithan, Ranji and Brill, Downey and Thorneloe, Susan and Ham, Robert}, year={1999}, month={Jul}, pages={195–201} }
 @article{heidari_ranjithan_1998, title={A hybrid optimization approach to thf estimation of distributed parameters in two-dimensional confined aquifers}, volume={34}, ISSN={["1093-474X"]}, DOI={10.1111/j.1752-1688.1998.tb01525.x}, abstractNote={ABSTRACT:  In using non‐linear optimization techniques for estimation of parameters in a distributed ground water model, the initial values of the parameters and prior information about them play important roles. In this paper, the genetic algorithm (GA) is combined with the truncated‐Newton search technique to estimate groundwater parameters for a confined steady‐state ground water model. Use of prior information about the parameters is shown to be important in estimating correct or near‐correct values of parameters on a regional scale. The amount of prior information needed for an accurate solution is estimated by evaluation of the sensitivity of the performance function to the parameters. For the example presented here, it is experimentally demonstrated that only one piece of prior information of the least sensitive parameter is sufficient to arrive at the global or near‐global optimum solution. For hydraulic head data with measurement errors, the error in the estimation of parameters increases as the standard deviation of the errors increases. Results from our experiments show that, in general, the accuracy of the estimated parameters depends on the level of noise in the hydraulic head data and the initial values used in the truncated‐Newton search technique.}, number={4}, journal={JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION}, author={Heidari, M and Ranjithan, SR}, year={1998}, month={Aug}, pages={909–920} }
 @inproceedings{ranjithan_barlaz_brill_dumas_harrison_kosmicki_solano_1998, title={Development of alternative solid waste management options with economic and environmental considerations: A mathematical modeling approach}, number={1998 Oct.}, booktitle={International Solid Waste Association 1998 World Congress, Charlotte, NC, Oct. 26-29, 1998}, author={Ranjithan, S. R and Barlaz, M. A. and Brill, E. D. and Dumas, R. D. and Harrison, K. W. and Kosmicki, B. A. and Solano, E.}, year={1998} }
 @inbook{lee_kim_ranjithan_1998, title={Dynamic analysis-based approach to determine flexible pavement layer moduli using deflection basin parameters}, ISBN={0309065119}, DOI={10.3141/1639-04}, abstractNote={ Most of the deflection analysis programs used today to analyze falling weight deflectometer (FWD) data are based on static analysis, which often underestimates the subgrade strength. Unfortunately, dynamic analysis usually involves complex calculations and requires significant computation time, thus making it impractical for routine applications. A methodology based on deflection basin parameters and artificial neural networks (ANN) for processing dynamic FWD measurements to estimate layer strengths is presented in this paper. Two-dimensional, dynamic finite element analysis using the ABAQUS program was employed to develop the deflection information for this study. Unlike the majority of the existing backcalculation programs that iteratively adjust the layer moduli to match the measured deflections, the proposed method first determines the subgrade modulus by means of two deflection basin parameters—Base Damage Index and Shape Factor F2—and then applies the estimated subgrade modulus and other parameters as input variables to a trained ANN to estimate the upper layers’ moduli. In contrast to other programs that require the input of seed values for layer moduli, this method does not require initial estimates as input. A set of field FWD measurements were analyzed both by this method and by the MODULUS program. Results reveal that the proposed method is able to better predict the asphalt concrete layer modulus while taking into account the dynamic effects of the FWD test. This method is also shown to be computationally efficient, which makes it applicable for routine tasks and field use. }, number={1639}, booktitle={Recent pavement research issues}, publisher={Washington, DC: Transportation Research Board}, author={Lee, Y. C. and Kim, Y. R. and Ranjithan, S. R.}, year={1998}, pages={36–42} }
 @inproceedings{harrell_ranjithan_1998, title={Generation of alternative strategies for insightful decision making in watershed management}, booktitle={Water resources and the urban environment: Proceedings of the 25th Annual Conference on Water Resources Planning and Management (ASCE), Chicago, IL, June 7-10, 1998}, author={Harrell, L. J. and Ranjithan, S.}, year={1998} }
 @inproceedings{harrison_ranjithan_al._1998, title={Interactively exploring efficient solid waste management alternatives to meet environmental goals}, booktitle={Coordination: Water resources and environment: Proceedings of special session of ASCE's 25th Annual Conference on Water Resources Planning and Management and the 1998 Annual Conference on Environmental Engineering, June 1998, Chicago, Illinois}, publisher={Reston, Va.: American Society of Civil Engineers}, author={Harrison, K. W. and Ranjithan, S. and al.}, year={1998} }
 @inbook{eldessouki_rouphail_beja_ranjithan_1998, title={Multiperiod highway improvement and construction scheduling: Model development and application}, ISBN={0309064627}, number={1617}, booktitle={Land use and transportation planning and programming applications}, publisher={Washington, DC: Transportation Research Board}, author={Eldessouki, W. and Rouphail, N. and Beja, M. and Ranjithan, S. R.}, year={1998}, pages={96–104} }
 @inproceedings{loughlin_ranjithan_brill_baugh_fine_1998, title={Prototype decision support tool for developing tropospheric ozone control strategies}, booktitle={Water resources and the urban environment-98: Proceedings of the 1998 National Conference on Environmental Engineering. ASCE Joint 25th Annual Conference on Water Resources Planning and Management and 1998 National Conference on Environmental Engineering, Chicago, IL, June 7-10,1998}, publisher={Reston, VA: American Society of Civil Engineers}, author={Loughlin, D. and Ranjithan, S. and Brill, E. D. and Baugh, J. and Fine, S.}, year={1998} }
 @inbook{harrell_ranjithan_1997, title={Generating efficient watershed management strategies using a genetic algorithms-based method}, booktitle={Aesthetics in the constructed environment: Proceedings of 24th Annual Water Resources Planning and Management Conference Houston, Texas, April 6-9, 1997}, publisher={New York: American Society of Civil Engineers}, author={Harrell, L. J. and Ranjithan, S.}, year={1997}, pages={272–277} }
 @inproceedings{loughlin_ranjithan_1997, title={The Neighborhood Constraint Method: A genetic algorithm-based multiobjective optimization technique}, volume={7}, booktitle={Genetic algorithms: Proceedings of the Seventh International Conference on Genetic Algorithms, East Lansing, Michigan, July 19-23, 1997}, publisher={San Francisco, Calif.: M. Kaufmann}, author={Loughlin, D. H. and Ranjithan, S.}, year={1997}, pages={666–673} }
 @article{dunstan_ranjithan_bernold_1996, title={Neural network model for the automated control of springback in rebars}, volume={11}, ISSN={0885-9000}, url={http://dx.doi.org/10.1109/64.511776}, DOI={10.1109/64.511776}, abstractNote={Automating the process of cold-bending steel reinforcing bars for concrete structures can save time, prevent serious injuries, and increase productivity. Neural network models can play a key role in the adaptive control of this process.}, number={4}, journal={IEEE Expert}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Dunstan, P.S. and Ranjithan, S.R. and Bernold, L.E.}, year={1996}, month={Aug}, pages={45–49} }
 @article{cieniawski_eheart_ranjithan_1995, title={Using Genetic Algorithms to Solve a Multiobjective Groundwater Monitoring Problem}, volume={31}, ISSN={0043-1397}, url={http://dx.doi.org/10.1029/94wr02039}, DOI={10.1029/94wr02039}, abstractNote={This paper builds on the work of Meyer and Brill (1988) and subsequent work by Meyer et al. (1990, 1992) on the optimal location of a network of groundwater monitoring wells under conditions of uncertainty. We investigate a method of optimization using genetic algorithms (GAs) which allows us to consider the two objectives of Meyer et al. (1992), maximizing reliability and minimizing contaminated area at the time of first detection, separately yet simultaneously. The GA‐based solution method has the advantage of being able to generate both convex and nonconvex points of the trade‐off curve, accommodate nonlinearities in the two objective functions, and not be restricted by the peculiarities of a weighted objective function. Furthermore, GAs have the ability to generate large portions of the trade‐off curve in a single iteration and may be more efficient than methods that generate only a single point at a time. Four different codings of genetic algorithms are investigated, and their performance in generating the multiobjective trade‐off curve is evaluated for the groundwater monitoring problem using an example data set. The GA formulations are compared with each other and also with simulated annealing on both performance and computational intensity. Simulated annealing relies on a weighted objective function which can find only a single point along the trade‐off curve for each iteration, while all of the multiple‐objective GA formulations are able to find a larger number of convex and nonconvex points of trade‐off curve in a single iteration. Each iteration of simulated annealing is approximately five times faster than an iteration of the genetic algorithm, but several simulated annealing iterations are required to generate a trade‐off curve. GAs are able to find a larger number of nondominated points on the trade‐off curve, while simulated annealing finds fewer points but with a wider range of objective function values. None of the GA formulations demonstrated the ability to generate the entire trade‐off curve in a single iteration. Through manipulation of GA parameters certain sections of the trade‐off curve can be targeted for better performance, but as performance improves at one section it suffers at another. Run times for all GA formulations were similar in magnitude.}, number={2}, journal={Water Resources Research}, publisher={American Geophysical Union (AGU)}, author={Cieniawski, Scott E. and Eheart, J. Wayland and Ranjithan, S.}, year={1995}, month={Feb}, pages={399–409} }
 @article{ritzel_eheart_ranjithan_1994, title={Using genetic algorithms to solve a multiple objective groundwater pollution containment problem}, volume={30}, ISSN={0043-1397}, url={http://dx.doi.org/10.1029/93wr03511}, DOI={10.1029/93wr03511}, abstractNote={The genetic algorithm (GA), a new search technique, is applied to a multiple objective groundwater pollution containment problem. This problem involves finding the set of optimal solutions on the trade‐off curve between the reliability and cost of a hydraulic containment system. The decision variables are how many wells to install, where to install them, and how much to pump from each. The GA is an optimization technique patterned after the biological processes of natural selection and evolution. A GA operates on a population of decision variable sets. Through the application of three specialized genetic operators: selection, crossover, and mutation, a GA population “evolves” toward an optimal solution. In the paper, simple GAs and GAs that can solve multiple objective problems are described. Two variations of a multiple objective GA are formulated: a vector‐evaluated GA (VEGA) and a Pareto GA. For the zero‐fixed cost situation, the Pareto GA is shown to be superior to the VEGA and is shown to produce a trade‐off curve similar to that obtained via another optimization technique, mixed integer chance constrained programming (MICCP). The effect on the VEGA and Pareto GA of parameter variation is shown. The Pareto GA is shown to be capable of incorporating the fixed costs associated with installing a system of wells. Results for several levels of fixed cost are presented. A comparison of computer resources required by the GAs and the MICCP method is given. Future research plans are discussed, including the incorporation of the objective of pump‐out time into the model and the development of parallelized GAs.}, number={5}, journal={Water Resources Research}, publisher={American Geophysical Union (AGU)}, author={Ritzel, Brian J. and Eheart, J. Wayland and Ranjithan, S.}, year={1994}, month={May}, pages={1589–1603} }
 @article{michielssen_sajer_ranjithan_mittra_1993, title={Design of lightweight, broad-band microwave absorbers using genetic algorithms}, volume={41}, ISSN={0018-9480}, url={http://dx.doi.org/10.1109/22.238519}, DOI={10.1109/22.238519}, abstractNote={A procedure for synthesizing multilayered radar absorbing coatings is presented. Given a predefined set of N/sub m/ available materials with frequency-dependent permittivities in /sub i/(f) and permeabilities mu /sub i/(f) (i=1,. . ., N/sub m/), the technique determines simultaneously the optimal material choice for each layer and its thickness. This optimal choice results in a screen which maximally absorbs TM and TE incident plane waves for a prescribed range of frequencies (f/sub 1/,f/sub 2/,. . ., f/sub N/f) and incident angles ( theta /sub 1/, theta /sub 2/,. . ., theta /sub N theta /). The synthesis technique is based on a genetic algorithm. The technique automatically places an upper bound on the total thickness of the coating, as well as the number of layers contained in it, which greatly simplifies manufacturing. In addition, the thickness or surface mass of the coating can be minimized simultaneously with the reflection coefficient. The algorithm was successfully applied to the synthesis of wideband absorbing coatings in the frequency ranges of 0.2-2 GHz and 2-8 GHz. >}, number={6}, journal={IEEE Transactions on Microwave Theory and Techniques}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Michielssen, E. and Sajer, J.-M. and Ranjithan, S. and Mittra, R.}, year={1993}, pages={1024–1031} }
 @article{garrett_case_hall_yerramareddy_herman_sun_ranjithan_westervelt_1993, title={Engineering applications of neural networks}, volume={4}, ISSN={0956-5515 1572-8145}, url={http://dx.doi.org/10.1007/bf00124977}, DOI={10.1007/bf00124977}, number={1}, journal={Journal of Intelligent Manufacturing}, publisher={Springer Nature}, author={Garrett, James H., Jr and Case, Michael P. and Hall, James W. and Yerramareddy, Sudhakar and Herman, Allen and Sun, Ruofei and Ranjithan, S. and Westervelt, James}, year={1993}, month={Feb}, pages={1–21} }
 @article{ranjithan_eheart_garrett_1993, title={Neural network-based screening for groundwater reclamation under uncertainty}, volume={29}, ISSN={0043-1397}, url={http://dx.doi.org/10.1029/92wr02129}, DOI={10.1029/92wr02129}, abstractNote={Uncertainty due to spatial variability of hydraulic conductivity is an important issue in the design of reliable groundwater remediation strategies. Using groundwater management models based on a stochastic approach to groundwater flow, where the log‐hydraulic conductivity is represented as a random field, is a frequently studied technique for the design of aquifer remediation in the presence of uncertainty. Such an approach employs the solution of a management model for a large set of equally probable realizations of the hydraulic conductivity. However, only a few out of the large set of realizations are critical to the final outcome of the design. The spatial distribution of the hydraulic conductivity values in a realization, and the degree of variation of the hydraulic conductivity values within a realization are identified as two important features that determine the level of criticalness of a realization. The association between the hydraulic conductivity pattern and the level of criticalness is not known explicitly and needs to be captured for efficient screening. The screening approach presented here utilizes the pattern classification capability of a neural network and its ability to learn from examples. It is shown that incorporation of only a few critical realizations in a groundwater management model can yield highly reliable remediation designs. The application of the screening tool in a pump‐and‐treat design problem is illustrated via two examples.}, number={3}, journal={Water Resources Research}, publisher={American Geophysical Union (AGU)}, author={Ranjithan, S. and Eheart, J. W. and Garrett, J. H., Jr.}, year={1993}, month={Mar}, pages={563–574} }
 @inbook{garrett_ranjithan_eheart_1992, place={New York}, title={Application of Neural Networks to Groundwater Remediation}, booktitle={Expert Systems in Civil Engineering - Knowledge Representation}, publisher={ASCE}, author={Garrett, JH, Jr and Ranjithan, S and Eheart, JW}, editor={Allen, REditor}, year={1992} }
 @article{michielssen_ranjithan_mittra_1992, title={Optimal multilayer filter design using real coded genetic algorithms}, volume={139}, ISSN={0267-3932}, url={http://dx.doi.org/10.1049/ip-j.1992.0070}, DOI={10.1049/ip-j.1992.0070}, abstractNote={A novel approach for designing optimal multilayer filters based on a real-coded genetic algorithm is presented. Given the total number of layers in the filter, as well as the electrical properties of the materials constituting each layer, the algorithm iteratively constructs multilayers whose frequency response closely matches a desired frequency response. In contrast to existing iterative techniques, this method does not require a preliminary design using classical techniques. Also, the design procedure is independent of the nature of the multilayer as well as the characteristics of the incident and substrate media. The algorithm is applied to the design of various lowpass and high-pass optical filters, operating between practical terminal conditions. The performance of the resulting designs matches or improves on that for filters that were synthesised using semiclassical techniques.}, number={6}, journal={IEE Proceedings J Optoelectronics}, publisher={Institution of Engineering and Technology (IET)}, author={Michielssen, E. and Ranjithan, S. and Mittra, R.}, year={1992}, pages={413} }
 @inproceedings{ranjithan_garrett_eheart_1991, place={Louis, Missouri}, title={A Feedback Neural Network Approach to Optimization: An Application in Groundwater Remediation Design}, booktitle={Intelligent Engineering Systems Through Artificial Neural Networks: Proceedings of the Int. Conference on Artificial Neural Networks in Engineering, St}, author={Ranjithan, S. and Garrett, J.H., Jr. and Eheart, J.W.}, editor={Dagli, CHEditor}, year={1991}, month={Nov} }
 @article{brill_flach_hopkins_ranjithan_1990, title={MGA: a decision support system for complex, incompletely defined problems}, volume={20}, ISSN={0018-9472}, url={http://dx.doi.org/10.1109/21.105076}, DOI={10.1109/21.105076}, abstractNote={Modeling-to-generate alternatives (MGA) is a technique for using mathematical programming models to generate a small number of different solutions for the decision maker to consider when dealing with complex, incompletely defined problems. The logic of MGA is presented in the context of concerns about the limitations of mathematical models and the human decision-makers who use them. Arguments and experimental evidence are presented to support the assumption that the human-machine decision-making system will perform better when the human is presented with a few different alternatives than when presented with a homogeneous set of alternatives, as might result from sensitivity analysis. >}, number={4}, journal={IEEE Transactions on Systems, Man, and Cybernetics}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Brill, E.D. and Flach, J.M. and Hopkins, L.D. and Ranjithan, S.}, year={1990}, pages={745–757} }
 @article{van oudheusden_ranjithan_singh_1987, title={The design of bus route systems — An interactive location-allocation approach}, volume={14}, ISSN={0049-4488 1572-9435}, url={http://dx.doi.org/10.1007/bf00837532}, DOI={10.1007/bf00837532}, number={3}, journal={Transportation}, publisher={Springer Science and Business Media LLC}, author={Van Oudheusden, D. L. and Ranjithan, S. and Singh, K. N.}, year={1987}, month={Sep}, pages={253–270} }