Numerical experiments on tsunami flow depth prediction for clustered areas using regression and machine learning models
国立国会図書館館内限定公開
収録元データベースで確認する
国立国会図書館デジタルコレクション
デジタルデータあり
公開元のウェブサイトで確認する
DOI[10.1186/s40623-022-01680-9]のデータに遷移します
全国の図書館の所蔵
国立国会図書館以外の全国の図書館の所蔵状況を表示します。
所蔵のある図書館から取寄せることが可能かなど、資料の利用方法は、ご自身が利用されるお近くの図書館へご相談ください
書誌情報
この資料の詳細や典拠（同じ主題の資料を指すキーワード、著者名）等を確認できます。
- 資料種別
- 記事
- 著者・編者
- Masato KamiyaYasuhiko IgarashiMasato Okada
- 出版年月日等
- 2022-08-17
- 出版年（W3CDTF）
- 2022-08-17
- タイトル（掲載誌）
- EPS : Earth, Planets and Space
- 巻号年月日等（掲載誌）
- 74(127)
- 掲載巻
- 74(127)
- ISSN（掲載誌）
- 1880-5981
- ISSN-L（掲載誌）
- 1343-8832
- 本文の言語コード
- eng
- DOI
- 10.1186/s40623-022-01680-9
- 国立国会図書館永続的識別子
- info:ndljp/pid/12685414
- コレクション（共通）
- コレクション（障害者向け資料：レベル1）
- コレクション（個別）
- 国立国会図書館デジタルコレクション > 電子書籍・電子雑誌 > その他
- 収集根拠
- オンライン資料収集制度
- 受理日（W3CDTF）
- 2023-03-07T16:17:01+09:00
- 保存日（W3CDTF）
- 2023-03-07
- 記録形式（IMT）
- application/pdf
- オンライン閲覧公開範囲
- 国立国会図書館内限定公開
- デジタル化資料送信
- 図書館・個人送信対象外
- 遠隔複写可否（NDL）
- 可
- 掲載誌（国立国会図書館永続的識別子）
- info:ndljp/pid/12287068
- 連携機関・データベース
- 国立国会図書館 : 国立国会図書館デジタルコレクション
- 要約等
- 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.
- DOI
- 10.1186/s40623-022-01680-9
- オンライン閲覧公開範囲
- インターネット公開
- 著作権情報
- 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/.
- 関連情報（URI）
- 参照
- Recent advances in earthquake seismology using machine learningComparative Performance of Scenario Superposition by Sequential Bayesian Update for Tsunami Risk Evaluation高性能・多機能津波計算コードJAGURSの開発深層学習を用いた断層諸元に基づく津波水位推定モデルの構築
- 参照
- Maximum tsunami height prediction using pressure gauge data by a Gaussian process at Owase in the Kii Peninsula, JapanA nonlinear parametric model based on a power law relationship for predicting the coastal tsunami heightParallel Implementation of Dispersive Tsunami Wave Modeling with a Nesting Algorithm for the 2011 Tohoku TsunamiLarge-scale, high-speed tsunami prediction for the Great Nankai Trough Earthquake on the K computerEarly forecasting of tsunami inundation from tsunami and geodetic observation data with convolutional neural networksMachine Learning Algorithms for Real-time Tsunami Inundation Forecasting: A Case Study in Nankai RegionNationwide Post Event Survey and Analysis of the 2011 Tohoku Earthquake TsunamiSuccessive estimation of a tsunami wavefield without earthquake source data: A data assimilation approach toward real‐time tsunami forecastingGreen's Function‐Based Tsunami Data Assimilation: A Fast Data Assimilation Approach Toward Tsunami Early WarningAn updated digital model of plate boundariesSurface deformation due to shear and tensile faults in a half-spaceGeneric Mapping Tools: Improved Version ReleasedTime and Space Distribution of Coseismic Slip of the 2011 Tohoku Earthquake as Inferred from Tsunami Waveform DataArtificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciencesNear-field tsunami amplification factors in the Kii Peninsula, Japan for Dense Oceanfloor Network for Earthquakes and Tsunamis (DONET)S-Net project: Performance of a Large-Scale Seafloor Observation Network for Preventing and Reducing Seismic and Tsunami DisastersFunction minimization by conjugate gradientsThe 2011 Magnitude 9.0 Tohoku-Oki Earthquake: Mosaicking the Megathrust from Seconds to CenturiesTsunami generation by horizontal displacement of ocean bottomAdvanced Real Time Monitoring System and Simulation Researches for Earthquakes and Tsunamis in JapanTsunami waveform inversion incorporating permanent seafloor deformation and its application to tsunami forecastingAlgorithm AS 136: A K-Means Clustering AlgorithmAdam: a method for stochastic optimizationFusion of Real-Time Disaster Simulation and Big Data Assimilation – Recent ProgressNear-field tsunami forecasting from cabled ocean bottom pressure dataMulti-index method using offshore ocean-bottom pressure data for real-time tsunami forecastData assimilation with dispersive tsunami model : a test for the Nankai TroughA rupture model of the 2011 off the Pacific coast of Tohoku Earthquake
- 連携機関・データベース
- 国立情報学研究所 : CiNii Research
- 提供元機関・データベース
- 学術機関リポジトリデータベース雑誌記事索引データベースCrossref科学研究費助成事業データベース科学研究費助成事業データベースCrossrefCrossrefCrossrefCrossref
- 書誌ID（NDLBibID）
- 12685414