基于智能优化算法的SWMM模型参数率定研究OA

To solve the problem of difficult parameter calibration for the SWMM model,an automatic calibration algo-rithm of PSO-SQP was established and implemented.The model parameters were calibrated by using PSO and PSO-SQP re-spectively 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 basi-cally consistent with the actual situation,but the peak error is larger;the accuracy of the calibrated parameters by both algo-rithms 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 accu-racy in calibration results,providing a scientific and effective new approach for parameter calibration of the SWMM model.