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Rainfall-Runoff Modelling Based on Long Short-Term Memory (LSTM)

Author(s): Weihong Liao, Xiaohui Lei, Ruojia Wang, Xiaohui Lei

Linked Author(s): Xiaohui Lei, Xiaohui Lei

Keywords: Rainfall-runoff modelling; Hydrological forecast; Machine learning; Deep learning; LSTM;

Abstract: The Long Short-Term Memory (LSTM) is suitable for rainfall-runoff modelling since it has a strong ability in fitting time series. In this study the LSTM was employed in predicting runoff in different foresight periods, in order to assess the capability of the LSTM in rainfall-runoff modelling and forecasting. The historical precipitation, meteorological and hydrological data were used as input data, runoff at after different foresight periods were selected as model output. The calibration period is 14 years and the validation period is 2 years. As expected, the proposed model shows a great ability to predict runoff 0~2 days ahead. With 3 days of foresight period, the LSTM performs relatively poor but still better than the hydrological model Xinanjiang. The number of hidden nodes has a priority impact on the prediction accuracy and t raining efficiency. While the length of input data has impact on model performance only when the foresight period is 0 day.


Year: 2019

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