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<p>Relief wells’ performance gradually decreases over time due to physical and chemical clogging during the operations. Pumping at relief wells is an economic and feasible method to improve the performance of relief wells instead of installing new wells. This paper proposed multi-objective optimization approaches for determining relief well pumping strategy (e.g., pumping rate and the number of pumping wells). Firstly, a three-dimensional transient seepage of Yangtze River levee with relief wells is simulated using MODFLOW model. The well pumping strategy is optimized by minimizing the average safety factor deficit to a defined threshold at the cross-section of relief wells, minimizing total pumping rate and minimizing the number of pumping wells. Then, non-dominated sorting genetic algorithm-II (NSGA-II) is used to derive the alternative optimal pumping strategies. To reduce enormous computational burden within the multi-objective optimization, a nonparametric regression procedure, i.e., multivariate adaptive regression splines (MARS), is used to establish an intelligent surrogate model for evaluating the safety factor of levee with relief wells instead of repetitive MODFLOW simulations. Finally, the best pumping strategy among Pareto solutions is selected by entropy weight and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The proposed approaches are tested on the Yangxin Yangtze River Dike in Hubei Province. Results show that the proposed approaches can objectively determine the final optimal relief well pumping operation by balancing the safety of levee and economic efficiency. Compared with the traditional method, the proposed approaches only require pumping groundwater from two wells for the study area, where the number of pumping wells is reduced by 50%, and the total pumping rate is decreased by 37.5%. Moreover, the proposed approaches significantly reduce the computation time from several thousand hours of repetitive numerical simulations to just one minute, providing a feasible tool for relief well maintenance.</p>

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Relief well pumping strategy for levee safety based on multivariate adaptive regression splines (MARS) and Non-dominated Sorting Genetic Algorithm II (NSGA-II)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月19日

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  • Mi Tian,
  • Hao Fan,
  • Awei An,
  • Xiaotao Sheng

摘要

Relief wells’ performance gradually decreases over time due to physical and chemical clogging during the operations. Pumping at relief wells is an economic and feasible method to improve the performance of relief wells instead of installing new wells. This paper proposed multi-objective optimization approaches for determining relief well pumping strategy (e.g., pumping rate and the number of pumping wells). Firstly, a three-dimensional transient seepage of Yangtze River levee with relief wells is simulated using MODFLOW model. The well pumping strategy is optimized by minimizing the average safety factor deficit to a defined threshold at the cross-section of relief wells, minimizing total pumping rate and minimizing the number of pumping wells. Then, non-dominated sorting genetic algorithm-II (NSGA-II) is used to derive the alternative optimal pumping strategies. To reduce enormous computational burden within the multi-objective optimization, a nonparametric regression procedure, i.e., multivariate adaptive regression splines (MARS), is used to establish an intelligent surrogate model for evaluating the safety factor of levee with relief wells instead of repetitive MODFLOW simulations. Finally, the best pumping strategy among Pareto solutions is selected by entropy weight and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The proposed approaches are tested on the Yangxin Yangtze River Dike in Hubei Province. Results show that the proposed approaches can objectively determine the final optimal relief well pumping operation by balancing the safety of levee and economic efficiency. Compared with the traditional method, the proposed approaches only require pumping groundwater from two wells for the study area, where the number of pumping wells is reduced by 50%, and the total pumping rate is decreased by 37.5%. Moreover, the proposed approaches significantly reduce the computation time from several thousand hours of repetitive numerical simulations to just one minute, providing a feasible tool for relief well maintenance.