A Novel mRMR-RFE-RF Method for Enhancing Medium- and Long-Term Hydrological Forecasting: A Case Study of the Danjiangkou Basin

In machine learning (ML)-based hydrological forecasting, particularly in medium- and long-term prediction, judicious predictor selection is paramount, as it ultimately determines the forecast accuracy. This study pioneered an advanced predictor-screening method that synergizes the mutual information (MI) and random forest (RF) technologies through minimum-redundancy-maximum-relevance-recursive feature elimination-random forest (mRMR-RFE-RF) method, blending both filtering and wrapping techniques. This method was rigorously tested through a detailed case study in the Danjiangkou basin, where a comprehensive analysis of 1560 meteorological factors was conducted. Employing three sophisticated ML algorithms-RF, eXtreme Gradient Boosting (XGB), and Light Gradient Boosting (LGB)-we developed precipitation forecasting models. Furthermore, we performed an in-depth rationality analysis of high-frequency predictors. The findings from our study show that this novel hybrid screening strategy markedly outperformed conventional singular predictor-screening methods in enhancing the accuracy of precipitation forecasting when integrated into these forecasting models. Moreover, it assured the validity of the high-frequency forecast factors employed. Therefore, this innovative method not only elevates the accuracy of medium- and long-term precipitation forecasting but also contributes a novel perspective to the methodology of predictor selection in hydrological forecasting models.