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dc.contributor.author | Mitra, Rimali | en |
dc.contributor.author | Naruse, Hajime | en |
dc.contributor.author | Abe, Tomoya | en |
dc.contributor.alternative | 成瀬, 元 | ja |
dc.date.accessioned | 2024-03-18T02:15:02Z | - |
dc.date.available | 2024-03-18T02:15:02Z | - |
dc.date.issued | 2024-02-08 | - |
dc.identifier.uri | http://hdl.handle.net/2433/287390 | - |
dc.description.abstract | The 2011 Tohoku-oki tsunami inundated the Joban coastal area in the Odaka region of the city of Minamisoma, up to 2818 m from the shoreline. In this study, the flow characteristics of the tsunami were reconstructed from deposits using the DNN (deep neural network) inverse model, suggesting that the tsunami inundation occurred in the Froude supercritical condition. The DNN inverse model effectively estimated the tsunami flow parameters in the Odaka region, including the maximum inundation distance, flow velocity, maximum flow depth, and sediment concentration. Despite having a few topographical anthropogenic undulations that caused the inundation height to fluctuate greatly, the reconstructed maximum flow depth and flow velocity were reasonable and close to the values reported in the field observations. The reconstructed data around the Odaka region were characterized by an extremely high velocity (12.1 m s⁻¹). This study suggests that the large fluctuation in flow depths on the Joban Coast compared with the stable flow depths in the Sendai Plain can be explained by the inundation in the supercritical flow condition. | en |
dc.language.iso | eng | - |
dc.publisher | Copernicus GmbH | en |
dc.rights | © Author(s) 2024. | en |
dc.rights | This work is distributed under the Creative Commons Attribution 4.0 License. | en |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.title | Understanding flow characteristics from tsunami deposits at Odaka, Joban Coast, using a deep neural network (DNN) inverse model | en |
dc.type | journal article | - |
dc.type.niitype | Journal Article | - |
dc.identifier.jtitle | Natural Hazards and Earth System Sciences | en |
dc.identifier.volume | 24 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 429 | - |
dc.identifier.epage | 444 | - |
dc.relation.doi | 10.5194/nhess-24-429-2024 | - |
dc.textversion | publisher | - |
dcterms.accessRights | open access | - |
datacite.awardNumber | 20H01985 | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-20H01985/ | - |
dc.identifier.eissn | 1684-9981 | - |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.awardTitle | タービダイトは地震・津波を記録するのか?:深層学習逆解析による解明 | ja |
出現コレクション: | 学術雑誌掲載論文等 |
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