Daily runoff time-series prediction based on the adaptive neural fuzzy inference system

Artificial neural network and fuzzy inference technology have been successfully used in various fields in the last decades. In order to combine the advantages of these approaches, the previous researchers came up with a new model named adaptive neural fuzzy inference system (ANFIS), which has been applied to signal processing and the related fields. Hydrological prediction is an important aspect of hydrological services for economy and society. The prediction result not only provides decision support for generation optimization, but also is of great significance to the economical operation of hydropower systems, navigation, flood control and so on. This paper presents the application of adaptive neural fuzzy inference system (ANFIS) on daily runoff time-series prediction at Tongzilin station. To evaluate the performances of the selected ANFIS, comparison was made with the ANN and autoregressive (AR) model. Previous inflows were chosen as input vectors of the three different models. Nash-Sutcliffe efficiency coefficient (NS coefficient), root mean square error (RMSE) and mean absolute relative error (MARE) were chosen to evaluate the performances of our models. The results show that ANFIS not only keep the potential of the ANN whose advantage is to deal with nonlinear problem, but it also eases the model building process and makes the result more stable. As a result, ANFIS can be a recommended daily runoff time-series prediction model.