Neural networks forecasting of flood discharge at an unmeasured station using river upstream information

Based upon information at stations upstream of a river, a back-propagation neural network model was employed in this study to forecast flood discharge at station downstream of the river which lacks measurement. The performance of the neural network model was evaluated from the indices of root mean square error, coefficient of efficiency, error of peak discharge, and error of time to peak. The verification results showed that the neural network model is preferable, which performs relatively better than that of the conventional Muskingum method. Furthermore, the developed model with different input parameters was trained to check the sensitivity of physiographical factors. The results exhibited that flood discharge and water stage, are two factors to dominate the accuracy of estimation. Meanwhile, the physiographical factors had a slight and positive influence on the accuracy of the prediction. The time varied flood discharge forecasting at an unmeasured station might provide a valuable reference for designing an engineering project in the vicinity of the investigation region.