Affiliation:
1. School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
2. Sichuan Hydraulic Research Institute, Chengdu 610072, China
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, 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.
Funder
National Key R&D Program of China
Natural Science Foundation of Hubei Province of China
Subject
Water Science and Technology,Aquatic Science,Geography, Planning and Development,Biochemistry
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