A stacking ensemble learning model for monthly rainfall prediction in the Taihu basin, China

The main objective of this study is to develop a stacking ensemble model for monthly rainfall prediction with multiple predictors and to examine the performance of the model. Specially, four machine learning models (KNN, XGB, SVR, ANN) were utilized as base learners due to their high popularity and good performance on previous studies. By means of assigning weights, the four base learners were combined to the stacking ensemble model.

The results show that the performance of base models varied among the nine stations in the Taihu Basin, while the stacking approach generally performed better than the four base models. The stacking model showed better performance in spring and winter than in summer and autumn. During wet months, the accuracy of model prediction varied more significantly.