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

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@Article{Han:2021:WRM,

• author = "Zheng Han and Wenxi Lu and Yue Fan and Jianan Xu and Jin Lin",
• title = "Surrogate-Based Stochastic Multiobjective Optimization for Coastal Aquifer Management under Parameter Uncertainty",
• journal = "Water Resources Management",
• year = "2021",
• volume = "35",
• pages = "1479--1497",
• keywords = "genetic algorithms, genetic programming, multigene genetic programming, seawater intrusion, uncertainty, simulation-optimisation, groundwater management, multiobjective evolutionary algorithm",
• publisher = "springer",
• bibsource = "OAI-PMH server at oai.repec.org",
• identifier = "RePEc:spr:waterr:v:35:y:2021:i:5:d:10.1007_s11269-021-02796-5",
• oai = "oai:RePEc:spr:waterr:v:35:y:2021:i:5:d:10.1007_s11269-021-02796-5",
• URL = "http://link.springer.com/10.1007/s11269-021-02796-5",
• DOI = "doi:10.1007/s11269-021-02796-5",
• abstract = "Linked simulation-optimisation (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 maximising the total pumping rate, minimising the total injection rate, and minimising the solute mass increase are considered in the optimisation model. The uncertain parameters are contained in both the constraints and the objective functions. A multiple realization approach is used to address the uncertainty in the model parameters, and a new multiobjective evolutionary algorithm (EN-NSGA2) is proposed to solve the optimisation 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.",

}

Genetic Programming entries for Zheng Han Wenxi Lu Yue Fan Jianan Xu Jin Lin

Citations