Long-Term prediction of discharges in manwan reservoir using artificial neural network models

Authors:
Chuntian Cheng;Kwokwing Chau;Yingguang Sun;Jianyi Lin
Affiliations:
Institute of Hydroinformatics, Department of Civil Engineering, Dalian University of Technology, Dalian, Liaoning, China;Department of Civil and Structural Engineering, Hong Kong Polytechnic University, Hong Kong, China;Institute of Hydroinformatics, Department of Civil Engineering, Dalian University of Technology, Dalian, Liaoning, China;Institute of Hydroinformatics, Department of Civil Engineering, Dalian University of Technology, Dalian, Liaoning, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

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Abstract

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.