YANGMeng-qi,XUZhi-wei,WANGYi-ming,et al.Deep LSTM Neural Network Model for Real-time Control of Urban Drainage System[J].China Water & Wastewater,2023,39(1):105-110.
Deep LSTM Neural Network Model for Real-time Control of Urban Drainage System
China Water & Wastewater[ISSN:1000-4062/CN:12-1073/TU]
volume:
第39卷
Number:
第1期
Page:
105-110
Column:
Date of publication:
2023-01-01
- Keywords:
- urban drainage system; real-time control; long short term memory (LSTM) neural network; deep learning; water level in front of pumping station
- Abstract:
- An urgent problem in the context of real?time control of drainage system is to establish a predicting model which balances operation time and prediction effect. To solve this problem, a deep long short term memory (LSTM) neural network model for real-time control of urban drainage system was constructed, which had strong nonlinear mapping ability and fast operation speed. The prediction performance and operation efficiency of the model were verified in Fuxing area of Suzhou City. The Nash-Sutcliffe efficiency coefficient of the prediction results of the water level in front of 18 pumping stations was above 0.5, and good fitting results were obtained under different rainfall scenarios. Compared with the mechanism model, the proposed model saved 99.7% of the operation time and significantly improved the real-time performance of the drainage system prediction model.
Last Update:
2023-01-01