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<p>Rapid urbanization has significantly increased impervious surfaces and exacerbated extreme rainfall events, leading to urban overflow and water ponding issues. To address these challenges, Low Impact Development (LID) facilities have been increasingly implemented as effective solutions. Storm Water Management Model (SWMM) model parameter calibration and LID facility layout optimization has emerged as a research focus. This paper establishes a SWMM model for a campus in Xi’an, China. Employing a coupled Back Propagation (BP) neural network for model parameter calibration. The SWMM model is coupled with the Non-dominated Sorting Genetic Algorithm III (NSGA-III) to construct an optimization model. The optimal LID layout scenario is selected using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) comprehensive evaluation method. Results demonstrate that the BP neural network-coupled calibration approach is both efficient and feasible. The proposed optimization model enhances the convergence of optimization results, and the TOPSIS method successfully identified the optimal solution. This study offers valuable insights for model parameter calibration and facility optimization.</p>

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Study on SWMM Calibration and Optimizing the Layout of LID Based on Intellgent Algorithm: A Case in Campus

  • 期刊论文

  • 发表日期：

    2025年4月8日

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  • Kehan Liu,
  • Jiake Li,
  • Jiayu Gao,
  • Xiaoqing Chi,
  • Chunbo Jiang

摘要

Rapid urbanization has significantly increased impervious surfaces and exacerbated extreme rainfall events, leading to urban overflow and water ponding issues. To address these challenges, Low Impact Development (LID) facilities have been increasingly implemented as effective solutions. Storm Water Management Model (SWMM) model parameter calibration and LID facility layout optimization has emerged as a research focus. This paper establishes a SWMM model for a campus in Xi’an, China. Employing a coupled Back Propagation (BP) neural network for model parameter calibration. The SWMM model is coupled with the Non-dominated Sorting Genetic Algorithm III (NSGA-III) to construct an optimization model. The optimal LID layout scenario is selected using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) comprehensive evaluation method. Results demonstrate that the BP neural network-coupled calibration approach is both efficient and feasible. The proposed optimization model enhances the convergence of optimization results, and the TOPSIS method successfully identified the optimal solution. This study offers valuable insights for model parameter calibration and facility optimization.