A novel information changing rate and conditional mutual information-based input feature selection method for artificial intelligence drought prediction models

The efficient and accurate selection of primary drought-driving factors as the independent variables of drought prediction model is critical in improving drought prediction accuracy. In this study, a novel feature selection method based on information changing rate and conditional mutual information (ICR-CMIFS) was proposed and evaluated by the comparison with other feature selection methods from feature selection, simulation, and classification aspects; two artificial intelligence drought prediction models, which treated the factors selected by ICR-CMIFS, correlation analysis (CA) and mutual information maximum (MIM) respectively as independent variables and standardized precipitation evapotranspiration index (SPEI) in 3/6/12-month time scales as dependent variables, were established; the superiority of ICR-CMIFS over CA and MIM methods in the selection of primary climatic drought-driving factors in Yunnan-Guizhou Plateau (YGP) was tested by the performance of the two models. The results revealed: the ICR-CMIFS was superior to the other feature selection methods; both artificial intelligence drought prediction models with the independent variables selected by ICR-CMIFS performed better in terms of correlation coefficient, Nash–Sutcliffe coefficient, root-mean square error and model computing time than by the MIM and CA methods. The outputs can provide an innovative approach in selecting primary drought-driving factors and improving drought prediction accuracy.