Modeling the Time Variation of Reservoir Trap Efficiency
Publication: Journal of Hydrologic Engineering
Volume 15, Issue 12
Abstract
All reservoirs are subjected to sediment inflow and deposition to a certain extent resulting in reduction of their capacity. Trap efficiency , a most important parameter for reservoir sedimentation studies, is being estimated using conventional empirical methods till today. A limited research has been carried out on estimating the variation of with time. In the present study, an attempt has been made to incorporate the age of the reservoir to estimate the . This study investigates the suitability of conventional empirical approaches along with soft computing data-driven techniques to estimate the reservoir . The incorporation of reservoir age, in empirical model, has resulted in a better estimation. Further, to estimate at different time steps, soft computing approaches such as artificial neural networks (ANNs) and genetic programming (GP) have been attempted. Based on correlation analysis, it was found that ANN model (4–4-1) resulted better than conventional empirical methods but inferior to GP. The results show that the GP model is parsimonious and understandable and is well suited to estimate of a large reservoir.
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Acknowledgments
The writers would like to thank BBMB for providing data to carry out this work. They gratefully acknowledge the anonymous reviewers and editors for their valuable comments and suggestions.
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Published In
Journal of Hydrologic Engineering
Volume 15 • Issue 12 • December 2010
Pages: 1001 - 1015
Copyright
© 2010 ASCE.
History
Received: Aug 27, 2008
Accepted: Apr 30, 2010
Published online: May 21, 2010
Published in print: Dec 2010
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