KANGDe-jun,LAILi-bao-yi,QIUFu-jie,et al.Automatic Calibration Method of SWMM Parameters Based on Machine Learning Theory[J].China Water & Wastewater,2024,40(5):122-129.
Automatic Calibration Method of SWMM Parameters Based on Machine Learning Theory
China Water & Wastewater[ISSN:1000-4062/CN:12-1073/TU]
volume:
第40卷
Number:
第5期
Page:
122-129
Column:
Date of publication:
2024-03-01
- Keywords:
- machine learning theory; Metropolis-Hastings algorithm; storm water management model (SWMM); MATLAB; automatic parameter calibration
- Abstract:
- As a kind of software for simulating rainfall situation, errors of SWMM will accumulate continuously with the operation of the model, and ultimately affect the simulation results of the model due to the uncertainty in the selection of model parameters within the empirical range. The traditional parameter calibration methods have the disadvantages such as low efficiency and poor accuracy. Therefore, this paper completed the data interaction and integration between MATLAB and SWMM by using MATLAB to call the hydraulic calculus program of SWMM, selected the parameters with high sensitivity as the calibration object by using multiple stepwise regression method, and eventually realized the automatic calibration of SWMM parameters by using Bayes-MH machine learning algorithm and numerical experiment based on the sensitivity analysis. In different measured rainfall scenarios, the results of automatic calibration were output smoothly, and the parameter matching degree was more than 95%, indicating that the method had excellent stability, self-adaptability and full-range parameter searching ability.
Last Update:
2024-03-01