Effects of Stochastic Simulations on Multiobjective Optimization of Groundwater Remediation Design under Uncertainty
Publication: Journal of Hydrologic Engineering
Volume 22, Issue 8
Abstract
The first step in probabilistic multiobjective groundwater optimization is the conceptualization of the uncertain conductivity () field. The distribution can be considered as a lognormal random variable. Two geostatistical approaches for conditional field generation, sequential Gaussian simulation (SGSIM) and sequential indicator simulation (SISIM), are used to generate sets of multiple conditional realizations of the field. The SGSIM- and SISIM-generated fields are found to have similar statistical properties, but SGSIM results in a smoother field. The solutions to a probabilistic multiobjective optimization of groundwater remediation design case study are addressed by combining the probabilistic improved niched Pareto genetic algorithm (PINPGA) with the stochastic groundwater flow and transport model. The multiobjective optimization of groundwater remediation design for the removal of trichloroethylene (TCE) plume at Massachusetts Military Reservation (MMR) in Cape Cod is used as a test case. The impact of the sets of realizations are evaluated by comparing the first and second moments of the resulting three-dimensional (3D) contaminant plume and by comparing the resulting Pareto frontiers. The resulting contaminant plumes of the two techniques have similar centroids, but the standard deviation and maximum concentrations are higher with the SISIM conductivity fields. Although PINPGA is able to identify valuable Pareto-optimal strategies about uncertain remediation options given either approach to generating -fields, the choice of conductivity field model impacted the Pareto frontiers. The optimization results demonstrate that with the smoothed SGSIM-based realizations, the PINPGA methodology obtains a smaller range between the upper and lower confidence intervals of the Pareto frontiers.
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Acknowledgments
This study is financially supported by the National Key Research and Development Program of China (No. 2016YFC0402807), the National Natural Science Foundation of China (Nos. 41402198 and 41372235), Jiangsu Natural Science Fund—Youth Fund (No. BK20131009), and the Fundamental Research Funds for the Central Universities (No. 2014B03614). The numerical calculations in this paper have been implemented on the IBM Blade cluster system in the High Performance Computing Center of Nanjing University. The authors also thank Prof. Chunmiao Zheng of South University of Science and Technology of China who provided the calibrated flow and transport model for establishing the multiobjective simulation-optimization model for the field site at MMR, Massachusettes. Also, the authors are profoundly grateful to the Editor and two anonymous reviewers whose invaluable suggestions have led to significant improvement of this manuscript.
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Published In
Journal of Hydrologic Engineering
Volume 22 • Issue 8 • August 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: Feb 11, 2016
Accepted: Dec 5, 2016
Published online: Apr 8, 2017
Published in print: Aug 1, 2017
Discussion open until: Sep 8, 2017
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