In this study, an artificial neural networks (ANN) was used to optimise the results obtained from a hydrodynamic model of river flow prediction. The study area is Reynolds Creek Experimental Watershed in southwest Idaho, USA. First a hydrodynamic model was constructed to predict flow at the outlet using time series data from upstream gauging sites as boundary conditions. The model, then was replaced with an ANN model using the same inputs. Finally a hybrid model was employed in which the error of the hydrodynamic model is predicted using an ANN model to optimise the outputs. Simulations were carried out for two different conditions (with and without data from a recently suspended gauging site) to evaluate the effect of this suspension in hydrodynamic, ANN and the hybrid model. Using ANN in this way, the error produced by the hydrodynamic model was predicted and thereby, the results of the model were improved.

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