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

Author

Listed:
  • Zheng Han

    (Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education
    Jilin University
    Jilin University)

  • Wenxi Lu

    (Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education
    Jilin University
    Jilin University)

  • Yue Fan

    (Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education
    Jilin University
    Jilin University)

  • Jianan Xu

    (Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education
    Jilin University
    Jilin University)

  • Jin Lin

    (Nanjing Hydraulic Research Institute)

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.

Suggested Citation

  • Zheng Han & Wenxi Lu & Yue Fan & Jianan Xu & Jin Lin, 2021. "Surrogate-Based Stochastic Multiobjective Optimization for Coastal Aquifer Management under Parameter Uncertainty," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(5), pages 1479-1497, March.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:5:d:10.1007_s11269-021-02796-5
    DOI: 10.1007/s11269-021-02796-5
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    References listed on IDEAS

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    1. Om Prakash Vats & Bhrigumani Sharma & Juergen Stamm & Rajib Kumar Bhattacharjya, 2020. "Groundwater Circulation Well for Controlling Saltwater Intrusion in Coastal aquifers: Numerical study with Experimental Validation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(11), pages 3551-3563, September.
    2. G. Kopsiaftis & V. Christelis & A. Mantoglou, 2019. "Comparison of Sharp Interface to Variable Density Models in Pumping Optimisation of Coastal Aquifers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(4), pages 1397-1409, March.
    3. Vasileios Christelis & Aristotelis Mantoglou, 2019. "Pumping Optimization of Coastal Aquifers Using Seawater Intrusion Models of Variable-Fidelity and Evolutionary Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 555-568, January.
    4. Yue Fan & Wenxi Lu & Tiansheng Miao & Jiuhui Li & Jin Lin, 2020. "Optimum Design of a Seawater Intrusion Monitoring Scheme Based on the Image Quality Assessment Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(8), pages 2485-2502, June.
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    Cited by:

    1. Shuangsheng Zhang & Jing Qiang & Hanhu Liu & Xiaonan Wang & Junjie Zhou & Dongliang Fan, 2022. "An Adaptive Dynamic Kriging Surrogate Model for Application to the Optimal Remediation of Contaminated Groundwater," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(13), pages 5011-5032, October.

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