Runoff prediction using hydro-meteorological variables and a new hybrid ANFIS-GPR model

Abstract

Precise and credible runoff forecasting is extraordinarily vital for various activities of water resources deployment and implementation. The neoteric contribution of the current article is to develop a hybrid model (ANFIS-GPR) based on adaptive neuro-fuzzy inference system (ANFIS) and Gaussian process regression (GPR) for monthly runoff forecasting in the Beiru river of China, and the optimal input schemes of the models are discussed in detail. Firstly, variables related to runoff are selected from the precipitation, soil moisture content, and evaporation as the first set of input schemes according to correlation analysis (CA). Secondly, principal component analysis (PCA) is used to eliminate the redundant information between the original input variables for forming the second set of input schemes. Finally, the runoff is predicted based on different input schemes and different models, and the prediction performance is compared comprehensively. The results show that the input schemes jointly established by CA and PCA (CA-PCA) can greatly improve the prediction accuracy. ANFIS-GPR displays the best forecasting performance among all the peer models. In the single models, the performance of GPR is better than that of ANFIS.