Automatic SWMM Parameter Calibration Method Based on the Differential Evolution and Bayesian Optimization Algorithm

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 capab...

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Bibliographic Details
Published inWater (Basel) Vol. 15; no. 20; p. 3582
Main Authors Gao, Jiawei, Liang, Ji, Lu, Yu, Zhou, Ruilong, Lu, Xin
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.10.2023
Subjects
Accuracy
Algorithms
Bayesian theory
Calibration
Comparative analysis
Contamination
Discovery and exploration
Drainage
Genetic algorithms
Markov analysis
Mathematical optimization
Methods
Open source software
Optimization algorithms
Outer space
Parameter estimation
Rain
Rain and rainfall
Simulation
simulation models
Stormwater
stormwater management
water
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Summary: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, the differential evolution algorithm, and the 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 the 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.
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ISSN:2073-4441
2073-4441
DOI:10.3390/w15203582