YUAN Shao-chun,LI Di,CHEN Yao,et al.Automatic Calibration Procedure of Storm Water Management Model Parameters Based on Back Propagation Neural Network Algorithm[J].China Water & Wastewater,2021,37(21):125-130.
基于BP神经网络算法的SWMM参数自动率定方法
- Title:
- Automatic Calibration Procedure of Storm Water Management Model Parameters Based on Back Propagation Neural Network Algorithm
- 关键词:
- 海绵城市; 暴雨洪水管理模型（SWMM）; BP神经网络; MATLAB; 参数率定
- Keywords:
- sponge city; storm water management model (SWMM); back propagation (BP) neural network; MATLAB; parameter calibration
- 摘要:
- 暴雨洪水管理模型（SWMM）的模拟准确性取决于其参数率定结果，而传统的线性和非线性数学回归方法难以得到全域最优解。为提高参数率定的准确性，利用SWMM的计算引擎调用函数与MATLAB软件平台进行耦合，采用拉丁超立方抽样法对待率定参数进行抽样形成参数组导入模型，最终利用BP神经网络进行训练完成模型参数的自动率定过程。案例研究结果表明，BP神经网络算法可有效完成参数的自动率定过程，且只需经历5次迭代便可获得变量间的映射关系，在4场不同强度降雨条件下SWMM的Nash-Sutcliffe效率系数(NS)均大于0.85。该方法能够得出与实测结果最接近的参数组合，模拟精度及稳定性良好，可用于实际工程的参数率定。
- Abstract:
- The simulation accuracy of SWMM model is greatly affected by its calibrated parameter settings, while mathematical programming techniques such as linear and nonlinear programming are difficult to arrive at the overall optimum. In order to improve the accuracy of parameter calibration, the call functions of the SWMM calculation engine were coupled with the MATLAB software. The Latin hypercube sampling method was used to sample calibrated parameters to form the parameter groups, and then imported to the SWMM model. Finally, back propagation (BP) neural network training was conducted to complete the automatic calibration process of model parameters. The results of case study showed that the BP neural network algorithm could effectively complete the automatic parameter calibration process, and the mapping relationship between variables could be obtained after only 5 iterations. The Nash-Sutcliffe efficiency coefficient (NS) of the SWMM were all greater than 0.85 under four rainfalls with different intensity. Results indicated that this method could obtain the parameter settings that closed to the observed, with good simulation accuracy and stability, and could be used in the parameter calibration process of actual engineering project.
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