Evaluation of Support Vector Machine and Adaptive Neuro-Fuzzy Inference System Performance in Prediction of Monthly River Flow (Case Study: Nazlu chai and Sezar Rivers)

In recent years by growing technology, new methods have been substantially developed to solve nonlinear problems such as river flow forecasting. Among the available various methods, Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models have been recently used by many researchers. In this study, these methods were used to predict the monthly flow of NazluChai and Sezar Rivers during 1956-2016 period. Firstly, the data were prepared in two modes: (a) using flow data and considering the role of memory; (b) influencing the periodic term. Modeling was done by 80% of the data (576 months) for training and the remaining 20% (144 months) for testing. The root mean square error (RMSE), Nash-Sutcliffe (NS) and mean absolute relative error (MARE) metrics were used to evaluate the performance of the proposed models. The results showed that the SVM method with the RBF kernel function had the best performance in predicting monthly flow of the studied rivers. In addition, the periodic term significantly increased the prediction accuracy of the SVM-RBF model. Also, the performance of the ANFIS method was improved by using the periodic term and this model had the least error in estimating the monthly flow of the Saesar and Nazlu chi Rivers in M6 and M7 patterns, respectively. In general, the results of this study showed that the SVM method performs better than the ANFIS model in monthly flow prediction and the selection of appropriate kernel function has a direct effect on its efficiency.