Bagged stepwise cluster analysis for probabilistic river flow prediction

Abstract

River flow prediction plays a crucial role in flood control and water resource management. As an effective approach, the stepwise cluster analysis (SCA) has been well-accepted in river flow prediction but still encounters difficulties to acquire accurate predictions in sample-sparse regimes. This study proposes a new ensemble model by incorporating the bagging method with the SCA. The proposed Bagged Stepwise Cluster Analysis (BSCA) is applied to predict daily river flow in the Upper Min River, China. The results show that BSCA effectively improves the prediction accuracy of SCA. Particularly, it addresses the limited end clusters problem of SCA and thus enhances the extreme-value prediction. It also has a better overall prediction performance over the classical random forest and decision tree models. In addition, BSCA is capable of quantifying uncertainties through a probabilistic prediction, which can be used to assess risks of extreme flows and provide reliable river flow predictions. The proposed new model can be applied to other water systems and environmental engineering problems.