Application of Informer Model Based on SPEI for Drought Forecasting

Abstract

To increase the accuracy of drought prediction, this study proposes a drought forecasting method based on the Informer model. Taking the Yellow River Basin as an example, the forecasting accuracies of the Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Informer models on multiple timescales of the Standardized Precipitation Evapotranspiration Index (SPEI) were compared and analyzed. The results indicate that, with an increasing timescale, the forecasting accuracies of the ARIMA, LSTM, and Informer models improved gradually, reaching the best accuracy on the 24-month timescale. However, the predicted values of ARIMA, as well as those of LSTM, were significantly different from the true SPEI values on the 1-month timescale. The Informer model was more accurate than the ARIMA and LSTM models on all timescales, indicating that Informer can widely capture the information of the input series over time and is more effective in long-term prediction problems. Furthermore, Informer can significantly enhance the precision of SPEI prediction. The predicted values of the Informer model were closer to the true SPEI values, and the forecasted SPEI trends complied with the actual trends. The Informer model can model different timescales adaptively and, therefore, better capture relevance on different timecales. The NSE values of the Informer model for the four meteorological stations on SPEI24 were 0.968, 0.974, 0.972, and 0.986.