| Item type |
文献 / Documents(1) |
| 公開日 |
2022-11-28 |
| アクセス権 |
|
|
アクセス権 |
open access |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 出版社版DOI |
|
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|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1186/s40623-022-01680-9 |
|
|
言語 |
ja |
|
|
関連名称 |
10.1186/s40623-022-01680-9 |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| タイトル |
|
|
タイトル |
Numerical experiments on tsunami flow depth prediction for clustered areas using regression and machine learning models |
|
言語 |
en |
| 著者 |
カミヤ, マサト
イガラシ, ヤスヒコ
オカダ, マサト
馬場, 俊孝
|
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
Emergency responses during a massive tsunami disaster require information on the flow depth of land for rescue operations. This study aims to predict tsunami flow depth distribution in real time using regression and machine learning. Training data of 3480 earthquake-induced tsunamis in the Nankai Trough were constructed by numerical simulations. Initially, the k-means method was used to discriminate the areas with approximately the same flow depth. The number of clustered areas was 18, and the standard deviation of the flow depth data in a cluster was 0.46 m on average. The objective variables were the mean and standard deviation of the flow depth in the clustered areas. The explanatory variables were the maximum deviation of the water pressure at the seafloor observation points of the DONET observatory. We generated multiple regression equations for a power law using these datasets and the conjugate gradient method. Further, we employed the multilayer perceptron method, a machine learning technique, to evaluate the prediction performance. Both methods accurately predicted the tsunami flow depth calculated by testing 11 earthquake scenarios in the cabinet office of the government of Japan. The RMSE between the predicted and the true (via forward tsunami calculations) values of the mean flow depth ranged from 0.34–1.08 m. In addition to large-scale tsunami prediction systems, prediction methods with a robust and light computational load as used in this study are essential to prepare for unforeseen situations during large-scale earthquakes and tsunami disasters. |
|
言語 |
en |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Tsunami prediction |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Regression |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Power law |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Multilayer perceptron |
| 書誌情報 |
en : Earth, Planets and Space
巻 74,
号 1,
p. 127,
発行日 2022-08-17
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| 収録物ID |
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収録物識別子タイプ |
ISSN |
|
収録物識別子 |
18805981 |
| 収録物ID |
|
|
収録物識別子タイプ |
NCID |
|
収録物識別子 |
AA11211921 |
| 出版者 |
|
|
出版者 |
BioMed Central |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
Springer Nature |
|
言語 |
en |
| 権利情報 |
|
|
言語 |
en |
|
権利情報 |
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| EID |
|
|
識別子 |
399810 |
|
識別子タイプ |
URI |
| 言語 |
|
|
言語 |
eng |
eps_74_1_127.pdf (1.59 MB)
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