Multiple Linear Regression and Artificial Neural Networks Models for Generalized Reservoir Storage–Yield–Reliability Function for Reservoir Planning
Publication: Journal of Hydrologic Engineering
Volume 14, Issue 7
Abstract
Generalized models for predicting the storage–yield–reliability functions of surface water reservoirs are developed using multiple linear regression and multilayer perceptron, artificial neural networks (ANNs). Linear regression was used to model the total capacity using the over-year capacity as one of the inputs. However, the ANNs were used to simultaneously model directly the intrinsically nonlinear over-year and total (i.e., within- -year) capacity-yield-reliability functions. The inputs used for the ANNs were basic runoff and systems variables such as the coefficient of variation of annual and monthly runoff, minimum monthly runoff, the demand ratio, and reliability. The results showed that all the models performed well during development and when tested with independent data sets. Both models offer avenues for predicting reservoir capacity at gauged sites without the expense of time-series based simulation alternatives.
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Acknowledgments
The writer is grateful to all the anonymous reviewers, the editor-in-chief, and the section editor for their helpful comments on an earlier draft of the manuscript.
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Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 14 • Issue 7 • July 2009
Pages: 731 - 738
Copyright
© 2009 ASCE.
History
Received: Feb 21, 2008
Accepted: Oct 27, 2008
Published online: Feb 19, 2009
Published in print: Jul 2009
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