Surrogate-Based Stochastic Multiobjective Optimization for Coastal Aquifer Management under Parameter Uncertainty

Zheng Han, Wenxi Lu (), Yue Fan, Jianan Xu and Jin Lin
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Zheng Han: Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education
Wenxi Lu: Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education
Yue Fan: Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education
Jianan Xu: Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education
Jin Lin: Nanjing Hydraulic Research Institute

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2021, vol. 35, issue 5, No 6, 1479-1497

Abstract: Abstract Linked simulation-optimization (S/O) approaches have been extensively used as tools in coastal aquifer management. However, parameter uncertainties in seawater intrusion (SI) simulation models often undermine the reliability of the derived solutions. In this study, a stochastic S/O framework is presented and applied to a real-world case of the Longkou coastal aquifer in China. The three conflicting objectives of maximizing the total pumping rate, minimizing the total injection rate, and minimizing the solute mass increase are considered in the optimization model. The uncertain parameters are contained in both the constraints and the objective functions. A multiple realization approach is utilized to address the uncertainty in the model parameters, and a new multiobjective evolutionary algorithm (EN-NSGA2) is proposed to solve the optimization model. EN-NSGA2 overcomes some inherent limitations in the traditional nondominated sorting genetic algorithm-II (NSGA-II) by introducing information entropy theory. The comparison results indicate that EN-NSGA2 can effectively ameliorate the diversity in Pareto-optimal solutions. For the computational challenge in the stochastic S/O process, a surrogate model based on the multigene genetic programming (MGGP) method is developed to substitute for the numerical simulation model. The results show that the MGGP surrogate model can tremendously reduce the computational burden while ensuring an acceptable level of accuracy.

Keywords: Seawater intrusion; Uncertainty; Simulation-optimization; Multigene genetic programming; Groundwater management; Multiobjective evolutionary algorithm (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11269-021-02796-5

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