Artificial Neural Network Prediction of Overtopping Rate for Impermeable Vertical Seawalls on Coral Reefs
Publication: Journal of Waterway, Port, Coastal, and Ocean Engineering
Volume 146, Issue 4
Abstract
An artificial neural network (ANN) tool trained using a backpropagation algorithm was developed to predict the overtopping rate of impermeable vertical seawalls on coral reefs. The training database was produced from simulations of a nonhydrostatic wave model calibrated using a subset of experimental overtopping data and covered a wide range of hydrological conditions, reef morphologies, and seawall heights. The ANN configuration was optimized through sensitivity analysis and overfitting was prevented using the k-fold cross-validation technique. The generalization ability of the ANN tool was tested against the remaining subset of the experimental data. The ANN tool provided reliable predictions using deep water wave parameters as input rather than parameters for waves at the toes of structures. This made it a practical predictor for use in the preliminary design of vertical seawalls and real time forecasting of wave-induced flooding in coral reef environments.
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Acknowledgments
The present study was inspired by a consulting project in which the first author participated along with employees of TIWTE. The authors would like to thank Dr. Zhigang Bai and Mr. Xingwu Zhang of Tianjin University for their kind help in conducting the physical experiment. This research work was financed by the National Natural Science Foundation of China (Grant No. 51779170) and Innovative Research Groups of the National Natural Science Foundation of China (Grant No. 51321065).
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Journal of Waterway, Port, Coastal, and Ocean Engineering
Volume 146 • Issue 4 • July 2020
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© 2020 American Society of Civil Engineers.
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Received: Feb 25, 2019
Accepted: Dec 11, 2019
Published online: Apr 17, 2020
Published in print: Jul 1, 2020
Discussion open until: Sep 17, 2020
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