@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{kanta_zechman_2014, title={Complex adaptive systems framework to assess supply-side and demand-side management for urban water resources}, volume={140}, number={1}, journal={Journal of Water Resources Planning and Management}, author={Kanta, L. and Zechman, E.}, year={2014}, pages={75–85} }
 @article{giacomoni_gomez_berglund_2014, title={Hydrologic impact assessment of land cover change and stormwater management using the hydrologic footprint residence}, volume={50}, number={5}, journal={Journal of the American Water Resources Association}, author={Giacomoni, M. H. and Gomez, R. and Berglund, E. Z.}, year={2014}, pages={1242–1256} }
 @article{rasekh_shafiee_zechman_brumbelow_2014, title={Sociotechnical risk assessment for water distribution system contamination threats}, volume={16}, number={3}, journal={Journal of Hydroinformatics}, author={Rasekh, A. and Shafiee, M. E. and Zechman, E. and Brumbelow, K.}, year={2014}, pages={531–549} }
 @article{shafiee_zechman_2013, title={An agent-based modeling framework for sociotechnical simulation of water distribution contamination events}, volume={15}, number={3}, journal={Journal of Hydroinformatics}, author={Shafiee, M. E. and Zechman, E. M.}, year={2013}, pages={862–880} }
 @article{zechman_giacomoni_shafiee_2013, title={An evolutionary algorithm approach to generate distinct sets of non-dominated solutions for wicked problems}, volume={26}, number={5-6}, journal={Engineering Applications of Artificial Intelligence}, author={Zechman, E. M. and Giacomoni, M. H. and Shafiee, M. E.}, year={2013}, pages={1442–1457} }
 @article{giacomoni_kanta_zechman_2013, title={Complex adaptive systems approach to simulate the sustainability of water resources and urbanization}, volume={139}, number={5}, journal={Journal of Water Resources Planning and Management}, author={Giacomoni, M. H. and Kanta, L. and Zechman, E. M.}, year={2013}, pages={554–564} }
 @article{zechman_2013, title={Integrating evolution strategies and genetic algorithms with agent-based modeling for flushing a contaminated water distribution system}, volume={15}, number={3}, journal={Journal of Hydroinformatics}, author={Zechman, E. M.}, year={2013}, pages={798–812} }
 @article{suresh_stoleru_zechman_shihada_2013, title={On event detection and localization in acyclic flow networks}, volume={43}, number={3}, journal={IEEE Transactions on Systems Man Cybernetics-Systems}, author={Suresh, M. A. and Stoleru, R. and Zechman, E. M. and Shihada, B.}, year={2013}, pages={708–723} }
 @article{damodaram_zechman_2013, title={Simulation-optimization approach to design low impact development for managing peak flow alterations in urbanizing watersheds}, volume={139}, number={3}, journal={Journal of Water Resources Planning and Management}, author={Damodaram, C. and Zechman, E. M.}, year={2013}, pages={290–298} }
 @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{giacomoni_zechman_brumbelow_2012, title={Hydrologic footprint residence: Environmentally friendly criteria for best management practices}, volume={17}, number={1}, journal={Journal of Hydrologic Engineering}, author={Giacomoni, M. H. and Zechman, E. M. and Brumbelow, K.}, year={2012}, pages={99–108} }
 @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{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} }
 @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{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} }
 @article{ormsbee_elshorbagy_zechman_2004, title={Methodology for pH total maximum daily loads: Application to beech creek watershed}, volume={130}, number={2}, journal={Journal of Environmental Engineering (New York, N.Y.)}, author={Ormsbee, L. and Elshorbagy, A. and Zechman, E.}, year={2004}, pages={167–174} }
 @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} }