In this study, a Constructive Neural Networks model (NSRBN) were used to forecast daily river flows for the Boa Esperan?a Hydroelectric power plant, part of the Chesf (Companhia Hidrel?trica do Sao Francisco) system. This dam is located at Parna?ba River, in the borderline between Maranhao and Piaui, two Brazilian States.

Several studies have been dedicated to the prediction of river flows with no exogenous inputs that are with the only use of past flow observations. In the present work, Constructive Neural Networks are first used without exogenous input that is without the use of rainfall observations. Only the last measured discharges are provided as input to the networks, analyzing the performance of the forecasts provided for the validation sets over the varying lead-times. In the second type of application, the same optimal number of past discharges is given as input to the ANN, along with exogenous inputs, that is past rainfall values, thus testing a rainfall-runoff modeling approach. The NSRBN model approach is shown to provide better representation of the daily average water inflow forecasting, than the models based on Box-Jenkins method, currently in use on the Brazilian Electrical Sector.