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.
2. Castellano-Mendez, M., W. lez-Manteiga, M. Febrero-Bande, J. Prada- Sanchez, and R. Lozano Caldero. 2004 .Modeling of the monthly and daily behavior of the runoff of the Xallas river using Box–Jenkins and neural networks methods Journal of Hydrology. 296. 38–58.
3. Cryer, D.j. and K.C. Chan. 2008. Time Series Analysis with Applications in R. Springer Texts in Statistics. Second Edition. 501pp.
4. Dong, X., F.M. Dohmen-Janssen, M. Booij and S. Hulscher. 2006. “Effect of flow forecasting quality on benefits of reservoir operation – a case study for the Geheyan reservoir (China).” Hydrology and Earth System Sciences Discussions.
5. Noakes, D.J., A.I. McLeod and K.W. Hipel. 1985. “Forecasting monthly riverflow time series.” International Journal of Forecasting, 1, 179-190.
6. Parker, D., S. Tunstall and T. Wilson. 2005. "Socio-economic benefits of flood forecasting and warning.” In Proceedings of the International Conference on Innovation, Advances and Implementation of Flood Forecasting Technology, October 17-19, 2005, Norway, Tromso.
7. Salas, J.D., J.W. Delleur, V. Yevjevich and W.L. Lane. 1980. Applied modeling of hydrologic time series. Water Resource Publication. 484 p.
8. Salas, J.D. and J.T. Obeysekera. 1982. ARMA model identification of hydrologic time series. Water Resour. Res. 18(4): 1011-1021.
9. Shalamu, A. 2009. Monthly and seasonal streamflow forecasting in the Rio Grande Basin. Doctor of Philosophy dissertation, New Mexico State University Las Cruces, New Mexico.
10. Tawfik, M. 2003. Linearity versus non-linearity in forecasting Nile River flows. Advances in Engineering Software 34. PP. 515-524.
12. Wang, W., P.H.A.J.M. van Gelder and J.K. Vrijling. 2005. Constructing prediction interval for monthly streamflow forecasts. In J. K. Vrijling et al. (Eds.) Proceedings of the 9th International Symposium on Stochastic Hydraulics, May 23- 24, 2005, Nijmegen, Netherlands. International Association of Hydraulic Research, Madrid, Spain.
13. Wang, W., P.H.A.J.M. Van-Gelder, J.K. Vrijling and J. Ma. 2006. Forecasting daily streamflow using hybrid ANN models. Journal of Hydrology 324 (2006) 383–399.