Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1278
Title: Long-term prediction of discharges in Manwan Reservoir using artificial neural network models
Authors: Cheng, C
Chau, KW 
Sun, Y
Lin, J
Keywords: Artificial neural networks
Algorithms
Backpropagation
Correlation methods
Discharge (fluid mechanics)
River flow discharges
Reservoirs (water)
Project management
Issue Date: 2005
Publisher: Springer Berlin / Heidelberg
Source: Lecture notes in computer science, 2005, v. 3498, p. 1040-1045 How to cite?
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.
URI: http://hdl.handle.net/10397/1278
ISBN: 978-3-540-25914-5
DOI: 10.1007/11427469_165
Rights: © Springer-Verlag Berlin Heidelberg 2005. The original publication is available at http://www.springerlink.com.
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