Long-Term Prediction of Discharges in Manwan Reservoir Using Artificial Neural Network Models
Several artificial neural network (ANN) models with a feed-forward, back-propagation network structure and various training algorithms, are developed to forecast daily and monthly river flow discharges in Manwan Reservoir. In order to test the applicability of these models, they are compared with a conventional time series flow prediction model. Results indicate that the ANN models provide better accuracy in forecasting river flow than does the auto-regression time series model. In particular, the scaled conjugate gradient algorithm furnishes the highest correlation coefficient and the smallest root mean square error. This ANN model is finally employed in the advanced water resource project of Yunnan Power Group.
KeywordsRoot Mean Square Error Artificial Neural Network Artificial Neural Network Model Time Series Model Normalize Root Mean Square Error
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