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Open AccessArticle

Modeling Maximum Tsunami Heights Using Bayesian Neural Networks

Department of Civil and Environmental Engineering, Hanyang University Seongdong-gu, Seoul 04763, Korea
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Atmosphere 2020, 11(11), 1266; https://doi.org/10.3390/atmos11111266
Received: 12 October 2020 / Revised: 13 November 2020 / Accepted: 16 November 2020 / Published: 23 November 2020
(This article belongs to the Special Issue Meteorological Extremes in Korea: Prediction, Assessment, and Impact)
Tsunamis are distinguished from ordinary waves and currents owing to their characteristic longer wavelengths. Although the occurrence frequency of tsunamis is low, it can contribute to the loss of a large number of human lives as well as property damage. To date, tsunami research has concentrated on developing numerical models to predict tsunami heights and run-up heights with improved accuracy because hydraulic experiments are associated with high costs for laboratory installation and maintenance. Recently, artificial intelligence has been developed and has revealed outstanding performance in science and engineering fields. In this study, we estimated the maximum tsunami heights for virtual tsunamis. Tsunami numerical simulation was performed to obtain tsunami height profiles for historical tsunamis and virtual tsunamis. Subsequently, Bayesian neural networks were employed to predict maximum tsunami heights for virtual tsunamis. View Full-Text
Keywords: tsunami; machine learning; bayesian neural networks; numerical simulation; maximum tsunami heights tsunami; machine learning; bayesian neural networks; numerical simulation; maximum tsunami heights
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MDPI and ACS Style

Song, M.-J.; Cho, Y.-S. Modeling Maximum Tsunami Heights Using Bayesian Neural Networks. Atmosphere 2020, 11, 1266. https://doi.org/10.3390/atmos11111266

AMA Style

Song M-J, Cho Y-S. Modeling Maximum Tsunami Heights Using Bayesian Neural Networks. Atmosphere. 2020; 11(11):1266. https://doi.org/10.3390/atmos11111266

Chicago/Turabian Style

Song, Min-Jong; Cho, Yong-Sik. 2020. "Modeling Maximum Tsunami Heights Using Bayesian Neural Networks" Atmosphere 11, no. 11: 1266. https://doi.org/10.3390/atmos11111266

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