Rainfall-runoff modelling and forecasting based on long short-term memory ( LSTM)

The Long Short-Term Memory ( LSTM ) is suitable for rainfall-runoff modelling and forecasting 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 hydr ological 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 Xinanjiang model. The number o f hidden nodes has a primary impact on the prediction accuracy and training efficiency . While the leng th of input data has an impact on model performance only when the foresight period is 0 day .

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