Numerical Model and Computational Intelligence Approaches for Estimating Flow through Rockfill Dam
Publication: Journal of Hydrologic Engineering
Volume 17, Issue 4
Abstract
A flood is a common natural disaster that causes enormous economic, social, and human losses. Over the years, a number of management approaches have been developed for lowering flood damage. A rock-fill dam is a suitable structure made of rocks for lowering the output hydrograph and controlling floods in watershed management. On the basis of experimental data, numerical method, artificial neural network (ANN), and neural network-genetic algorithm (NNGA) approaches were applied for predicting flow through trapezoidal and rectangular rock-fill dams. Input parameters for this prediction were selected on the basis of sensitivity analysis. According to the results of the sensitivity analysis, the heights of water in the upstream and downstream sides of the dams were considered as the inputs of the models. The results indicated that the application of a genetic algorithm for optimization of ANN parameters improved the flow estimates. The Delta-Bar-Delta algorithm presented a better performance compared with the other learning algorithms for ANN models. Meanwhile, the NNGA models trained with the Momentum learning algorithm gave the best flow estimates. In general, the used approaches performed well in estimating flow through rock-fill dam; however, the numerical method showed superiority over the other methods.
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Acknowledgments
The writers express their gratitude to Bu-Ali Sina University for its support. We wish to express our gratitude to the editor and anonymous reviewers whose suggestions and remarks have greatly helped us to improve the quality of the manuscript.
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Published In
Journal of Hydrologic Engineering
Volume 17 • Issue 4 • April 2012
Pages: 528 - 536
Copyright
© 2012. American Society of Civil Engineers.
History
Received: Apr 23, 2010
Accepted: May 25, 2011
Published online: May 27, 2011
Published in print: Apr 1, 2012
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