| 基于智能优化算法的SWMM模型参数率定研究 |
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| 引用本文：王伟1,陶慧青1，张含2，陈冲1.基于智能优化算法的SWMM模型参数率定研究[J].计算技术与自动化,2026,(2):154-158 |
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| 中文摘要:针对SWMM模型参数率定困难的问题，建立了PSO-SQP自动率定算法并实现。利用PSO、PSO-SQP对模型参数分别进行率定，设置不同收敛迭代次数，对各组参数的模拟结果进行对比分析，并利用PSO-SQP率定参数对2022年6月5日淳安县的降水过程进行验证。结果显示：率定期：PSO-SQP率定参数模拟效果最好，峰值流量和总流量相对误差均低于15%；PSO率定参数模拟效果与实况基本吻合，但峰值误差偏大；两种算法的率定参数准确性均与迭代次数呈正相关。验证期：PSO-SQP率定参数的模拟结果相对误差低于10%。结果表明PSO-SQP率定算法的率定结果准确率高，为SWMM模型参数率定提供科学有效的新思路。 |
| 中文关键词:SWMM 参数率定 PSO SQP |
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| Parameter Calibration of SWMM Model Based on Intelligent Optimization Algorithms |
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| Abstract:To solve the problem of difficult parameter calibration for the SWMM model, an automatic calibration algorithm of PSO-SQP was established and implemented. The model parameters were calibrated by using PSO and PSO-SQP respectively with different convergence iteration numbers. The simulation results of each group of parameters were compared and analyzed, and the calibrated parameters by PSO-SQP were used to verify the precipitation process on June 5, 2022 in Chun’an county. The results show that:calibration period: the simulation results of PSO-SQP calibrated parameters are the best, with peak flow and total flow relative errors below 15%; the simulation results of PSO calibrated parameters are basically consistent with the actual situation, but the peak error is larger; the accuracy of the calibrated parameters by both algorithms is positively correlated with the number of iterations.Verification period: the relative error of the simulation results of PSO-SQP calibrated parameters is below 10%. The results show that the PSO-SQP calibration algorithm has the high accuracy in calibration results, providing a scientific and effective new approach for parameter calibration of the SWMM model. |
| keywords:SWMM parameter calibration PSO SQP |
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