Prediction of Scour Downstream of Grade-Control Structures Using Neural Networks
Publication: Journal of Hydraulic Engineering
Volume 134, Issue 11
Abstract
A new approach for predicting local scour downstream of grade-control structures based on neural networks is presented. An explicit neural networks formulation (ENNF) is developed using a transfer function (sigmoid) and optimal weights obtained from a training process. A genetic algorithm was used to optimize the neural network architecture and the optimal weights for input and output parameters were obtained using the Levenberg–Marquardt back-propagation algorithm. Experimental data available in the literature, including large-scale results were used for training and validation of the proposed model. The predictive performance of the ENNF was found superior to other regression-based equations and the robustness of ENNF was evaluated using field data.
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Information & Authors
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Published In
Journal of Hydraulic Engineering
Volume 134 • Issue 11 • November 2008
Pages: 1656 - 1660
Copyright
© 2008 ASCE.
History
Received: Oct 30, 2006
Accepted: Mar 13, 2008
Published online: Nov 1, 2008
Published in print: Nov 2008
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