Application of a hybrid ARIMA-LSTM model based on the SPEI for drought forecasting

Abstract

Drought forecasting can effectively reduce the risk of drought. We proposed a hybrid model based on deep learning methods that integrates an autoregressive integrated moving average (ARIMA) model and a long short-term memory (LSTM) model to improve the accuracy of short-term drought prediction. Taking China as an example, this paper compares and analyzes the prediction accuracy of six drought prediction models, namely, ARIMA, support vector regression (SVR), LSTM, ARIMA-SVR, least square-SVR (LS-SVR), and ARIMA-LSTM, for standardized precipitation evapotranspiration index (SPEI). The performance of all the models was compared using measures of persistence, such as the Nash-Sutcliffe efficiency (NSE). The results show that all three hybrid models (ARIMA-SVR, LS-SVR, and ARIMA-LSTM) had higher prediction accuracy than the single model, for a given lead time, at different scales. The NSEs of the hybrid models for the predicted SPEI1 are 0.043, 0.168, and 0.368, respectively, and the NSEs of SPEI24 is 0.781, 0.543, and 0.93, respectively. This finding indicates that when the lead time remains unchanged, the hybrid model has high prediction accuracy for SPEI on long time scales and low prediction accuracy for SPEI on short time scales, and the prediction accuracy of the model with a 1-month lead time is higher than that of the model with a 2-month lead time. In addition, the ARIMA-LSTM model has the highest prediction accuracy at the 6-, 12-, and 24-month scales, indicating that the model is more suitable for the forecasting of long-term drought in China.