The potential of integrated hybrid pre-post-processing techniques for short- to long-term drought forecasting

Abstract

Abstract Due to the drought negative impacts, accurate forecasting of drought indices is important. This study focused on the short- to long-term Standardized Precipitation Index (SPI) forecasting in sites with different climates using newly integrated hybrid pre-post-processing techniques. Four sites in Iran's northwest were selected and the SPIs series with time scales of 3, 9, and 24 months were forecasted during the period of 1978–2017. For improving the modeling efficiency, wavelet transform and ensemble empirical mode decomposition (EEMD) pre-processing methods were used. In this regard, temporal features of the SPIs series were decomposed via wavelet transform (WT), then, the obtained sub-series were further broken down into intrinsic mode functions using EEMD. Also, simple linear averaging and nonlinear neural ensemble post-processing methods were applied to ensemble the outputs of hybrid models. The results showed that data pre-processing enhanced the models' capability up to 40%. Also, integrated pre-post-processing models improved the models' efficiency by approximately 50%. The root mean square errors' criteria distribution range decreased from 0.337–1.03 (in raw data) to 0.195–0.714 (in decomposed data). The results proved the capability of applied methods in modeling the SPIs series. In increasing the models' accuracy, data pre-processing was more effective than data post-processing.