عنوان مقاله [English]
One of the usual methods for stream flow prediction are time series models. In this research, an autoregressive integrated moving average model(ARIMA) is used for forecasting of daily inflow of Talezang station, located in upstream of Dez dam. As the data have seasonal trend, statistical indices (mean factors and standard deviation) of daily discharge, are estimated for 28 years with period of 365 days using Fourier series. Then, daily-observed discharge data were standardized using the statistical indices. The results of the research showed that seasonal trend of data was removed by the calculated factors of Fourier series. Then, different autoregressive integrated moving average modelswere fit into standardized data. Finally, by Akaike Information Criterion (AIC) and considering the minimum number of factors of model, the best model was selected. The results of forecasting by selected model showed that the model can forecast daily inflow trend relatively suitable and comparing with previous researches, mean absolute relative error of daily flow forecasting decreased from 3.12 to 0.6 and forecast lead-time increased from 10 days to two years.