Inflow forecasting plays an important role in reservoir operation and water resources management. In this paper, Artificial Neural Network (ANN) and multiple regression models have been used to forecast inflow into Dez reservoir using data from upstream hydrometric stations. The paper aims to define the best pattern of spatial and temporal correlations among the stations in upstream the reservoir. using the correlation coefficient and mean square of errors (MSE), The performances of different models were compared. The results indicited that the ANN forecasts the reservoir inflow better than the multiple regression models. The best one day prior forecasting was obtained using the data of the nearest station to the reservoir (Tangpanj station). Furthermore, the best three days prior forecasting model is obtained using Kamandab, Vanaee, Doroodtireh and Daretakht stations. Hence, by increasing the distance of forecaster stations from the target station, the forecasting time would increase from one day to three days, but forecasting accuracy would decrease by 42%.
Bani habib,M. E. , Mousavi,S. M. and Jamali,F. S. (2010). An artificial neural network model detecting daily correlation among the stations in reservoir inflow forecasting. Iranian Water Researches Journal, 4(2), 25-32.
MLA
Bani habib,M. E. , , Mousavi,S. M. , and Jamali,F. S. . "An artificial neural network model detecting daily correlation among the stations in reservoir inflow forecasting", Iranian Water Researches Journal, 4, 2, 2010, 25-32.
HARVARD
Bani habib M. E., Mousavi S. M., Jamali F. S. (2010). 'An artificial neural network model detecting daily correlation among the stations in reservoir inflow forecasting', Iranian Water Researches Journal, 4(2), pp. 25-32.
CHICAGO
M. E. Bani habib, S. M. Mousavi and F. S. Jamali, "An artificial neural network model detecting daily correlation among the stations in reservoir inflow forecasting," Iranian Water Researches Journal, 4 2 (2010): 25-32,
VANCOUVER
Bani habib M. E., Mousavi S. M., Jamali F. S. An artificial neural network model detecting daily correlation among the stations in reservoir inflow forecasting. IWRJ, 2010; 4(2): 25-32.
