Abstract
Seawater intrusion is a common problem in coastal areas. The rational distribution of groundwater exploitation can minimize the scope of seawater intrusion and maximize groundwater exploitation. In this study, an optimization method for the groundwater exploitation layout in coastal areas was proposed. Based on the numerical simulation model of variable-density groundwater, a multiobjective groundwater management model was constructed with the objectives of maximizing groundwater exploitation and minimizing seawater intrusion. The optimization model was solved by nondominated sorted genetic algorithm-II (NSGA-II). To improve the computational efficiency of the optimization model, the surrogate models of the groundwater simulation model were built by using three different methods: kriging, support vector regression (SVR), and kernel extreme learning machines (KELM). Finally, the above methods were tested in Longkou City of China. The results show that the use of surrogate models can greatly reduce the computing time for solving seawater intrusion management problems. The surrogate model of the variable-density groundwater simulation model based on the SVR method has the best performance. The groundwater exploitation layout optimized by the above method is reasonable and can reflect the actual hydrogeological conditions in the study area. This study provides a reliable way to optimize the groundwater exploitation layout in coastal areas.
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Funding
This study was supported by the National Key Research and Development Program of China (No.2016YFC0402800), the National Nature Science Foundation of China (No.41672232), and Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, China.
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Fan, Y., Lu, W., Miao, T. et al. Multiobjective optimization of the groundwater exploitation layout in coastal areas based on multiple surrogate models. Environ Sci Pollut Res 27, 19561–19576 (2020). https://doi.org/10.1007/s11356-020-08367-2
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DOI: https://doi.org/10.1007/s11356-020-08367-2