Improving Prediction of Dam Failure Peak Outflow Using Neuroevolution Combined with -Means Clustering
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Volume 22, Issue 6
Abstract
Estimation of peak outflow resulting from dam failure is of paramount importance for flood risk analysis. This paper presents a new hybrid clustering model based on artificial neural networks and genetic algorithms (ANN-GA) for improving predictions of peak outflow from breached embankment dams. The input layer of the ANN-based model comprises height and volume of water behind the breach at failure time plus a new parameter of cluster number. The cluster number is obtained from partitioning the input data set using the -means clustering technique. The model is demonstrated using the data samples collected from the literature and compared with three benchmark models by using a cross-validation method. The benchmark models consist of a conventional regression model and two ANN models trained by nonlinear techniques. Results indicate that the suggested model is able to estimate the peak outflows more accurately, especially for big flood events. The best prediction for the current database was obtained from a five-clustered ANN-GA model. The uncertainty analysis shows the five-clustered ANN-GA model has the lowest prediction error and the smallest uncertainty.
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Acknowledgments
The authors gratefully acknowledge the financial and other supports of this research provided by the Islamic Azad University, Islamshahr branch, Tehran, Iran, and also wish to thank the anonymous reviewers for making constructive comments that substantially improved the quality of the paper.
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Journal of Hydrologic Engineering
Volume 22 • Issue 6 • June 2017
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©2017 American Society of Civil Engineers.
History
Received: Feb 9, 2016
Accepted: Nov 14, 2016
Published online: Feb 21, 2017
Published in print: Jun 1, 2017
Discussion open until: Jul 21, 2017
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