LIU Xing-po,WANG Zhi-qiang,LI Jing.Global Sensitivity Analysis Method for Parameters of Storm Water Management Model Based on BP Neural Network[J].China Water & Wastewater,2021,37(9 9):122-129.
Global Sensitivity Analysis Method for Parameters of Storm Water Management Model Based on BP Neural Network
China Water & Wastewater[ISSN:1000-4062/CN:12-1073/TU]
volume:
第37卷
Number:
9 9
Page:
122-129
Column:
Date of publication:
2021-05-01
- Keywords:
- SWMM parameter; global sensitivity analysis; BP neural network; modified Morris method; LH-OAT (Latin hypercube one factor-at-a-time); linear regression method; Nash-Sutcliffe efficiency coefficient; set pair relational degree
- Abstract:
- In order to accurately and efficiently analyze the sensitivity of SWMM parameters and identify the key calibration parameters of the model, a global sensitivity analysis method based on BP neural network was proposed. Twelve parameters (such as catchment area, width, impermeable area ratio, etc.) were inputted into the model, and Nash-Sutcliffe efficiency coefficient (NSE) was used to represent the model output. Three sets of verification schemes and their comparison reference criteria were designed, and sensitivity analysis methods (such as BP neural network, modified Morris method, linear regression method, LH-OAT method, polynomial regression method, etc.) were compared and analyzed. When connection weight of BP neural network was used as the basis of global sensitivity analysis, the sensitivity analysis accuracy of BP neural network method was better than that of linear regression method, LH-OAT method and polynomial regression method. Therefore, BP neural network method was a feasible method for global sensitivity analysis of SWMM parameters. The parameter sensitivity analysis accuracy of SWMM was closely related to the non-linear fitting degree of the analysis model. Excellent non-linear fitting ability was an important reason why BP neural network was superior to other sensitivity analysis methods. By comparing the effects of different sensitivity analysis methods, it was proposed that Morris perturbation sensitivity analysis results were used as the true value benchmark, SWMM geometric parameters were used as sensitivity indicator parameters, and linear regression method and goodness of fit were used as comparison benchmark, which could provide reference for the effect evaluation of sensitivity analysis methods. The parameter sensitivity analysis method of SWMM based on BP neural network develops a new way to solve the multi-cycle calibration of parameter sensitivity analysis and optimization of complex models.
Last Update:
2021-05-01