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Automatic SWMM parameter calibration method based on the differential evolution and Bayesian optimization algorithm
  • +2
  • Gao Jiawei,
  • Lu Yu,
  • Zhou Ruilong,
  • Lu Xin,
  • Ji Liang
Gao Jiawei
Huazhong University of Science and Technology School of Civil and Hydraulic Engineering
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Lu Yu
Huazhong University of Science and Technology School of Civil and Hydraulic Engineering
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Zhou Ruilong
Huazhong University of Science and Technology School of Civil and Hydraulic Engineering
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Lu Xin
Sichuan Hydraulic Research Institute
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Ji Liang
Huazhong University of Science and Technology School of Civil and Hydraulic Engineering

Corresponding Author:[email protected]

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Abstract

In response to the low accuracy exhibited by the Storm Water Management Model (SWMM), we propose an enhanced Differential Evolution and Bayesian Optimization Algorithm (DE-BOA) . This algorithm integrates the global search capability of the differential evolution algorithm with the local search capability of the Bayesian optimization algorithm, which enables a more comprehensive exploration of the vector solution space. A comparative analysis of various types of rainfall events is conducted. For model calibration and validation, a drainage subzone in Jinshazhou, Guangzhou City, is selected as the research subject. In total, 20 specific rainfall events are selected, and the DE-BOA algorithm outperforms the manual calibration, differential evolution algorithm, and Bayesian optimization algorithm regarding model calibration accuracy. Furthermore, the DE-BOA algorithm exhibits robust adaptability to rainfall events characterized by multiple peaks and higher precipitation levels, with Nash–Sutcliffe Efficiency coefficient values surpassing 0.90. This study’s findings could hold significant reference value for dynamically updating model parameters, thereby enhancing the model simulation performance and improving the accuracy of the urban intelligent water management platform.