Long-term prediction of runoff of Heiher River in China based on EMD-LSTM networks

Abstract

Due to the influence of global climate change and human activities, the time series data of river runoff become complex and non-stationary. These properties make sequence prediction difficult and with low accuracy. In order to improve the prediction accuracy, Empirical Mode Decomposition (EMD) was introduced to Long Short-Term Memory (LSTM) networks in this paper. The EMD decomposed a non-stationary time series into multiple components. We trained an LSTM network for each component, and added their predictions to obtain the final forecasting value of original sequence. At last, the EMD-LSTM model was applied to the annual runoff sequence in the upper Heihe River. By comparing with single LSTM, it shows that the EMD-LSTM has higher accuracy for the long-term prediction of river runoff.