水利水电科技进展2025，Vol.45Issue(4)：31-38,8.DOI:10.3880/j.issn.1006-7647.2025.04.005
基于峰值导向型粒子群优化算法的城市水文模型自动率定方法
Automatic calibration method for urban hydrological models based on peak-oriented particle swarm optimization algorithm
摘要
Abstract
To address the issues of low efficiency and insufficient accuracy in manual calibration of runoff and peak time for multi-parameter urban hydrological models,this study proposed an automatic parameter calibration method based on the improved particle swarm optimization(PSO)algorithm.The method introduces Logistic mapping for particle initialization and Lévy flight for position updating within the PSO framework to avoid local optima.Additionally,considering the characteristics of urban runoff generation and concentration processes,a weighted multi-objective fitness function incorporating overall fitting,peak flow,and peak time was constructed to enhance the model's ability to capture key hydrological features.The proposed method was implemented in Python and coupled with a mechanistic model(storm water management model,SWMM).Using field monitoring data from a test site,ten key hydrological parameters were calibrated,and the performance of fitness functions with different weight assignments was compared.The results demonstrate that the weighted multi-objective fitness function is more advantageous for urban drainage system emergency management,particularly in improving the simulation accuracy of peak flow and peak time.When applied to a real drainage system in Jiujiang City,the method achieved peak flow and peak time errors of 0.56%and-6.82%,respectively,confirming its feasibility and accuracy.关键词
城市水文模型/多目标适应度函数/粒子群优化算法/自动率定/SWMMKey words
urban hydrological model/multi-objective fitness function/particle swarm optimization algorithm/automatic calibration/SWMM分类
建筑与水利引用本文复制引用
许王辰,陈瑞弘,孙岸炜..基于峰值导向型粒子群优化算法的城市水文模型自动率定方法[J].水利水电科技进展,2025,45(4):31-38,8.基金项目
上海勘测设计研究院有限公司项目(2022HJ(83)-011) （2022HJ(83）
