Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 조용식 | - |
dc.date.accessioned | 2021-12-03T02:23:28Z | - |
dc.date.available | 2021-12-03T02:23:28Z | - |
dc.date.issued | 2020-05 | - |
dc.identifier.citation | JOURNAL OF COASTAL RESEARCH, v. 95(sp1), page. 1291-1296 | en_US |
dc.identifier.issn | 0749-0208 | - |
dc.identifier.issn | 1551-5036 | - |
dc.identifier.uri | https://bioone.org/journals/journal-of-coastal-research/volume-95/issue-sp1/SI95-249.1/Probabilistic-Tsunami-Heights-Model-using-Bayesian-Machine-Learning/10.2112/SI95-249.1.short | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/166674 | - |
dc.description.abstract | Tsunamis, which are long-period oceanic waves, are known as catastrophic disasters and can cause large losses of human life, as well as property damage. To date, tsunami research has focused on developing numerical models to predict accurate tsunami heights and run-up heights, because hydraulic experiments are associated with high costs for laboratory installation and maintenance. Recently, artificial intelligence (AI) has been progressed, demonstrating enhanced performances in science and engineering fields. This study explored the use of AI to estimate maximum tsunami heights. Bayesian machine learning, a neural network method, was employed, and numerical simulation was performed for historical and probable maximum tsunami events. | en_US |
dc.description.sponsorship | This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIP) (No. 2015R1A2A1A15054097). | en_US |
dc.language.iso | en | en_US |
dc.publisher | COASTAL EDUCATION & RESEARCH FOUNDATION | en_US |
dc.subject | Tsunamis | en_US |
dc.subject | maximum tsunami heights | en_US |
dc.subject | Bayesian machine learning | en_US |
dc.subject | numerical simulation | en_US |
dc.title | Probabilistic Tsunami Heights Model using Bayesian Machine Learning | en_US |
dc.type | Article | en_US |
dc.relation.no | Special 95 | - |
dc.identifier.doi | 10.2112/SI95-249.1 | - |
dc.relation.page | 1291-1296 | - |
dc.relation.journal | JOURNAL OF COASTAL RESEARCH | - |
dc.contributor.googleauthor | Song, Min-Jong | - |
dc.contributor.googleauthor | Cho, Yong-Sik | - |
dc.relation.code | 2020045714 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING | - |
dc.identifier.pid | ysc59 | - |
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