Nonlinear forecasting of daily inflow using neural network

The daily inflow has one of apparent nonlinear and complicated phenomena. The nonlinearity and complexity make it difficult to model the prediction of daily flow, but attractive to try the neural networks approach which contains inherently nonlinear scheme. The study focuses on developing the forecasting models of daily inflows to a large dam site using neural networks. In order to reduce the error caused by high or low outliers, the Back propagation algorithm which is one of neural network structures is modified by combining the regression algorithm. The study indicates that continuous forecasting of a reservoir inflow in real time is possible through the use of modified neural network models. The positive effect of the modification using regression scheme in BP algorithm is showed in the low and high inflows.