نوع مقاله : یادداشت فنی (5 صفحه)
1 دانشجوی کارشناسی ارشد/ مهندسی عمران آب دانشگاه رازی، کرمانشاه، ایران
2 استاد/ گروه مهندسی عمران دانشگاه رازی، کرمانشاه، ایران
3 کارشناس ارشد/ مهندسی عمران آب، دانشگاه رازی، کرمانشاه، ایران
عنوان مقاله [English]
Regarding to the water resources reduction especially in Iran, the river flow forecasting has been very important and it is necessary to use the best methods. For this purpose, there are several linear and nonlinear methods. As monthly linearity and nonlinearity of inflow detection is difficult, in this study the performance of some linear and nonlinear models to predict the monthly flow of Jamishan river in Kermanshah province was investigated. These models include autoregressive integrated moving average (ARIMA), artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS). In using of ARIMA model with considering five parameters of any kind, all possible models were evaluated. For ANFIS and ANN models with determination of 14 different input combinations, the best models were identified. The capability of obtained models in the long-term time flow prediction was also assessed. The results revealed that ANFIS model is more capable compare to ANN in identification of effective time delays on flow. This model is also more accurate than other models in peak values prediction. Unlike it, ARIMA models showed high capability in prediction of low values. Studies indicated that all three models can be used for long-term time as well.