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Reliable and accurate streamflow forecasting plays a crucial role in water resources systems (WRS) especially in dams operation and watershed management. However, due to the high uncertainty associated WRS components and nonlinear nature of streamflow generations, the realistic streamflow forecasts is still one of the most challenging issue in WRS. This paper aimed to forecast one-month ahead streamflow of Karun river (Iran) by coupling an artificial neural network (ANN) with an improved binary version of gravitational search algorithm (IBGSA), named ANN- IBGSA. To this end, the best lag number for each predictor at Poleshaloo station was firstly selected by auto-correlation function (ACF). The ANN-IBGSA was used to minimize the sum of RMSE and R2 and to identify the optimal predictors. Finally, to characterize the hydro-climatic uncertainties associated with the selected predictors, an
implicit approach of Monte-Carlo simulation (MCS) was applied. The ACF plots indicated a significant correlation up to a lag of two months for the input predictors. The ANN-IBGSA identified the Tmean (t-1), Q(t-1) and Q(t) as the best predictors. Findings demonstrated that the ANN-IBGSA forecasts were considerably better than those previously carried out by researchers in 2013. The average improvement values were 9.91%, 11.85% and 9.13% for RMSE, R2 and MAE, respectively. The Monte-Carlo simulations demonstrated that all of forecasted values lie within the 95% confidence intervals.
 

Keywords: streamflow forecast, artificial neural network, uncertainty, gravitational search algorithm, Monte-Carlo simulation, Karun River

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