Performance of fuzzy logic in stream flow forecasting

River flow forecasting for a region has a special and important role for optimal allocation of water resources. In this research، for forecasting river flow process، Fuzzy Inference System (FIS) is used. Three parameters including precipitation، temperature and daily discharge are used for forecasting of daily river flow of Lighvan River located in Lighvanchai watershed. For the initial preprocessing، the randomness of data was examined by return points test. Then، for determination of the optimum lags for input parameters، correlogram of data was considered. Finally to investigate the effects of temperature on river flow forecasting، the process were done for any months separately. Assessments of prediction by using various criteria such as Nash-Sutcliff coefficient showed that FIS model had high precision (CNS=0. 9976) and low error (RMSE=0. 0113) in prediction which shows that the FIS model can be employed successfully in river flow forecasting. Final assessment of the results was also revealed the effects of temperature on prediction in some months (April and December).