@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{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} }