Intelligent identification of effective reservoirs based on the random forest classification model

Abstract

In the real-time operation of a flood control system, identifying effective reservoirs accurately and adaptively is the premise of establishing a multi-reservoir real-time flood control hybrid operation (MRFCHO) model. The existing effective reservoir intelligent reasoning (ERIR) method is based on the inference rules, which causes the identification results to be greatly influenced by the training samples. This paper establishes a random forest classification (RFC) model for identifying effective reservoirs to solve this problem. The performance of the RFC model is evaluated in terms of model stability and model accuracy, and compared with the ERIR model and other machine learning (ML) models. First, different flood samples are used to establish the models to verify the model stability. Then, the expected total cost under cross-validation is taken as the index to evaluate the classification accuracy. Finally, the average increase of expected total cost with cross-validation is proposed to evaluate the criteria importance and analyze the sensitive factors that affect the classification accuracy of the RFC model. The proposed method is applied to a multi-reservoir system in the Huaihe River basin in China. The results indicate that the RFC model has the characteristic of high classification accuracy, low sensitivity to flood samples and high stability. It displays more dominance in the dynamic identification of effective reservoirs compared to other models.