Drought forecasts could effectively reduce the risk of drought. Data-driven models are suitable forecast tools because of their minimal information requirements. The motivation for this study is that because most data-driven models, such as autoregressive integrated moving average (ARIMA) models, can capture linear relationships but cannot capture nonlinear relationships they are insufficient for long-term prediction.The hybrid ARIMA¿support vector regression (SVR) model proposed in this paper is based on the advantages of a linear model and a nonlinear model. The multi scale standard precipitation indices (SPI:SPI1, SPI3, SPI6, and SPI12) were forecast and compared using the ARIMA model and the hybrid ARIMA¿SVR model. The performance of all models was compared using measures of persistence, such as the coefficient of determination, root-mean-square error, mean absolute error, Nash¿Sutcliffe coefficient,and kriging interpolation method in the ArcGIS software.

Zhang Qi, big data analyst, research interests in drought prediction and spatial analysis visualization, senior training lecturer, graduated from the North China University of Water Resources and Electric in China, hobby of Chinese martial arts, was the chairman of the university's martial arts association.