CUIZhong-jie,QINGXiao-xia,YANGSen-xiong.Prediction of Urban Rainfall Runoff Based on DM-LSTM[J].China Water & Wastewater,2022,38(19):132-138.
Prediction of Urban Rainfall Runoff Based on DM-LSTM
China Water & Wastewater[ISSN:1000-4062/CN:12-1073/TU]
volume:
第38卷
Number:
第19期
Page:
132-138
Column:
Date of publication:
2022-10-01
- Keywords:
- urban rainfall runoff prediction; data-driven model; LSTM model; data mining（DM）; deep learning; smart water
- Abstract:
- Under the background of smart water, how to deepen the research on urban rainfall runoff model based on artificial intelligence theory and technology is a topic worthy of exploration. Due to the high temporal resolution of urban rainfall runoff and irregular distribution of sample features, it is challenging to directly use long short term memory (LSTM) model for prediction. Based on this backround, data mining (DM) algorithm and rules were proposed to cluster and reconstruct the urban rainfall runoff time series data sets, the structure and parameters of LSTM model were optimized based on deep learning algorithm, and a DM-LSTM coupling model was constructed and applied to simulate rainfall runoff in the study area. For all kinds of rainfall events, compared with the LSTM model, the root mean square error (RMSE) of the DM-LSTM coupling model was decreased by 2.1%-41.9%, the Nash-Sutcliffe effciency (NSE) coefficient was increased by 0.4%-56.4%, and the coefficient of determination (R2) was increased by 0.3%-65.6%. The DM-LSTM coupling model showed a better prediction performance for all kinds of rainfall events, and decreased the running time of the model to only 2.044 s, which could well meet the needs of real-time, accuracy and stability for urban rainfall runoff prediction.
Last Update:
2022-10-01