Hydrological forecasting and updating procedures for neural network

A non-linear Auto-Regressive Exogenous-input model (NARXM) for river flow forecasting by an output-updating procedure is presented. This updating procedure is based on the structure of a constructive neural network (NSRBN - A Non-linear Sigmoidal Regression Blocks Networks). The NARXM-neural network updating procedure is tested using the daily discharge forecasts of the routing (SSARR - Streamflow Synthesis And Reservoir Regulation) conceptual model operating on the Sao Francisco River having different discharge conditions. The performance of the NARXM-neural network updating procedure is compared with that of the linear Auto-Regressive Exogenous-input (ARXM) model updating procedure, the latter being a generalisation of the widely used Auto-Regressive (AR) model forecast error updating procedure. The results of the comparison indicate that the NARXM procedure performs better than the ARXM procedure.