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Rapid Prediction of Storm Wave Run-Up Using a Hybrid Physics-Informed Machine Learning
Storm-induced wave run-up is responsible for wave overtopping, beach erosion, and flooding. Therefore, it is crucial to simulate such events, especially during hurricanes and nor’easters. Low-fidelity phase-averaged models are often preferred and used for the prediction of wave run-up due to their computational efficiency. However, phase-resolving numerical models have shown great accuracy in predicting wave run-up with much demanding computation resources. In this study, a mapping approach based on machine learning techniques is proposed to rapidly predict high-fidelity numerical simulations given their corresponding low-fidelity results. Specifically, the proposed model maps the wave run-up from the phase-averaged surf-beat of the XBeach model to its corresponding values from the phase-resolving nonhydrostatic mode. Two artificial neural networks were trained to simulate the extreme wave run-up \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(R_{2\% } )$$\end{document} and wave profile, respectively. The simulation results demonstrate the excellent performance of the proposed model in predicting the wave run-up characteristics. As a result, the models are suitable for use in early warning systems, probabilistic risk assessment, and rapid prediction of wave run-up during extreme events.
Rapid Prediction of Storm Wave Run-Up Using a Hybrid Physics-Informed Machine Learning
Storm-induced wave run-up is responsible for wave overtopping, beach erosion, and flooding. Therefore, it is crucial to simulate such events, especially during hurricanes and nor’easters. Low-fidelity phase-averaged models are often preferred and used for the prediction of wave run-up due to their computational efficiency. However, phase-resolving numerical models have shown great accuracy in predicting wave run-up with much demanding computation resources. In this study, a mapping approach based on machine learning techniques is proposed to rapidly predict high-fidelity numerical simulations given their corresponding low-fidelity results. Specifically, the proposed model maps the wave run-up from the phase-averaged surf-beat of the XBeach model to its corresponding values from the phase-resolving nonhydrostatic mode. Two artificial neural networks were trained to simulate the extreme wave run-up \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(R_{2\% } )$$\end{document} and wave profile, respectively. The simulation results demonstrate the excellent performance of the proposed model in predicting the wave run-up characteristics. As a result, the models are suitable for use in early warning systems, probabilistic risk assessment, and rapid prediction of wave run-up during extreme events.
Rapid Prediction of Storm Wave Run-Up Using a Hybrid Physics-Informed Machine Learning
Lecture Notes in Civil Engineering
Desjardins, Serge (editor) / Azimi, Amir Hossein (editor) / Poitras, Gérard J. (editor) / Naeini, Saeed Saviz (author) / Snaiki, Reda (author)
Canadian Society of Civil Engineering Annual Conference ; 2023 ; Moncton, NB, Canada
Proceedings of the Canadian Society for Civil Engineering Annual Conference 2023, Volume 9 ; Chapter: 13 ; 179-190
2024-10-10
12 pages
Article/Chapter (Book)
Electronic Resource
English
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