Rainfall-runoff short-term forecasting method based on LSTM

Abstract

A rainfall-runoff forecasting method based on Long Short Term Memory (LSTM) was proposed in this study, which can extract the trend characteristics of runoff time series data through introducing daily rainfall data collected at related upstream stations and making use of the advantages of LSTM in saving long-term sequence feature information and avoid vanishing gradient, and identify the nonlinear mapping relationship in between, thus establishing a short-term runoff forecasting model. In this study, 24-hour and 5-day short-term forecasting models were established based on the runoff data collected at Danba Hydrologic Station in Dadu River Basin and historical rainfall collected at three upstream stations (Xiaojin, Dawei, and Fubian River). The experimental results showed that the forecasting models performed well during the inspection period. In 24-hour forecasting, RMSE was 98.016 and MAE was 45.709, which were 73.993 and 32.699, respectively in 5-day forecasting, indicating better performance and increased forecasting accuracy than simple LSTM model.