LI Shuang-yu,ZHANG Ming-kai,LIU Yan-chen,et al.Flow Prediction of Drainage System Based on Long Short Time Memory Model[J].China Water & Wastewater,2022,38(5):59-64.
Flow Prediction of Drainage System Based on Long Short Time Memory Model
China Water & Wastewater[ISSN:1000-4062/CN:12-1073/TU]
volume:
第38卷
Number:
第5期
Page:
59-64
Column:
Date of publication:
2022-03-01
- Keywords:
- drainage system; LSTM model; flow prediction; time sequence; optimal experimental parameter
- Abstract:
- Flow prediction of drainage systems is of great significance for urban water safety and optimal operation of wastewater treatment plants. Different from traditional hydrological models which need complex modeling and a large amount of geographic information data, machine learning can realize flow prediction and early warning of a drainage system through data driving. In combination with the time sequence of flow data, five long short time memory (LSTM) models (Vanilla LSTM, Stacked LSTM, Bidirectional LSTM, CNN LSTM and ConV LSTM) under the conditions of single variable (flow) and double variable (flow and rainfall) were applied to predict the inlet flow of a wastewater treatment plant in Wuxi City, Jiangsu Province. In the parameter selection experiment, the optimal parameter condition of Bidirectional LSTM was that the number of LSTM hidden layer units,training epochs and training set samples were 250, 200 and 250. Under the same condition, Bidirectional LSTM predicted the future flow more effectively than the other four methods. Compared with simulation with flow as the only variable, its accuracy of flow prediction was improved by nearly 20% after adding rainfall as another variable.
Last Update:
2022-03-01