Reservoir Sedimentation Estimation Using Artificial Neural Network
Publication: Journal of Hydrologic Engineering
Volume 14, Issue 9
Abstract
Conventional methods and models available for estimation of reservoir sedimentation process differ greatly in terms of complexity, inputs, and other requirements. An artificial neural network (ANN) model was used to estimate the volume of sediment retained in a reservoir. Annual rainfall, annual inflow, and capacity of the reservoir were chosen as inputs. Thirty Two years of data pertaining to Gobindsagar Reservoir on the Satluj River in India, were used in this study (23 years for training and 9 years for testing). The pattern of the sediment volume retained in this reservoir was well captured by the Multi-Layer Perceptron (3–5-1) ANN model using the back propagation algorithm. Based on several performance indices, it was found that the ANN model estimated the volume of sediment retained in the reservoir with better accuracy and less effort as compared to conventional regression analysis.
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Acknowledgments
The writers thank Bhakra Beas Management Board (BBMB) for providing necessary data to carry out this work. Special thanks are owed to Professor M. C. Deo, Department of Civil Engineering, Indian Institute of Technology Bombay, India, for his support and valuable comments. They gratefully acknowledge the anonymous reviewers and editors for their valuable reviews and suggestions.
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Information
Published In
Journal of Hydrologic Engineering
Volume 14 • Issue 9 • September 2009
Pages: 1035 - 1040
Copyright
© 2009 ASCE.
History
Received: Dec 9, 2007
Accepted: Jan 29, 2009
Published online: Aug 14, 2009
Published in print: Sep 2009
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