HEYuan-bin,FANGZheng,KANGDan,et al.Real-time Control Method for Combined Sewer Overflow Based on BP Neural Network[J].China Water & Wastewater,2024,40(3):120-123.
Real-time Control Method for Combined Sewer Overflow Based on BP Neural Network
China Water & Wastewater[ISSN:1000-4062/CN:12-1073/TU]
volume:
第40卷
Number:
第3期
Page:
120-123
Column:
Date of publication:
2024-02-01
- Keywords:
- combined sewer system; neural network; real-time control method; interceptor sewer overflow
- Abstract:
- This paper proposed a method for controlling combined sewer overflow based on BP neural network. The water level at the control point for a future time span was predicted by using the data of rainfall, water level at the control point, evaporation and antecedent dry period during every 5 minutes before the current time, and the gate overflow was implemented when the predicted water level at the control point exceeded the set value for an extended period. A total of 3 000 sets of observation data in recent 2 years were trained and tested. The mean absolute error (MAE) of the model was 0.111 5, the root mean square error (RMSE) was 0.156 5, the mean percentage error (MPE) was 0.045 3%, and the Nash-Sutcliffe efficiency coefficient (NSE) was 0.938 6, indicating that the model met the overall prediction requirements in application. The model was deployed in Wuhan Smart Water System. In the rainfall event on July 8, 2023, the water level was predicted to rise to more than 22.7 m, and the gate overflow was implemented, thereby avoiding waterlogging in the city.
Last Update:
2024-02-01