{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1390289940732681088.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.2208/kaigan.77.2_i_307"}},{"identifier":{"@type":"URI","@value":"https://www.jstage.jst.go.jp/article/kaigan/77/2/77_I_307/_pdf"}},{"identifier":{"@type":"NAID","@value":"130008113344"}}],"dc:title":[{"@language":"en","@value":"A Study on Tsunami Arrival Time Prediction by Machine Learning"},{"@language":"ja","@value":"機械学習による津波到達時間予測に関する検討"}],"dc:language":"ja","description":[{"type":"abstract","notation":[{"@language":"en","@value":"<p> In this study, as an initial step for instantaneous tsunami arrival time prediction after an earthquake, we used images of the initial water level obtained from numerical tsunami simulations for the Nankai Trough to predict the tsunami arrival time using machine learning. The arrival times were pre-processed and colored every 3 minutes and every 2 minutes for training. The accuracy of the tsunami arrival time prediction was improved by increasing the number of training data, and the error of the tsunami arrival time was within 3 minutes in some places. In addition, we compared the prediction accuracy of A town and B city in Mie prefecture to confirm the difference. Although the inundation area is overestimated and the arrival time is currently overestimated or underestimated, the arrival time of the tsunami is generally predicted well by machine learning based on the initial water level information.</p>"},{"@language":"ja","@value":"<p>　本検討では，地震が発生してから瞬時に津波到達時間予測を行うための初期段階として，南海トラフを対象とした津波数値シミュレーション結果から得た初期水位の画像を用いて，津波到達時間の予測を機械学習を用いて行った．津波到達時間は，前処理として3分おきと2分おきに色付けをして学習に用いた．津波到達時間の予測では，学習データを増やすことによって精度の向上がみられ，津波到達時間の誤差が3分以内に収まる場所も見受けられた．また，三重県A町とB市の比較を行い，予測精度の違いを確認した．津波到達時間の色付けの間隔を変えたケースでは，大きな違いは見られず，浸水範囲は過大評価，津波到達時間は現状過大もしくは過小評価となっているが，初期水位の情報から津波到達時間を機械学習によって概ね良好に予測できた．</p>"}],"abstractLicenseFlag":"disallow"}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1410289940732681091","@type":"Researcher","personIdentifier":[{"@type":"NRID","@value":"9000414252033"}],"foaf:name":[{"@language":"en","@value":"GUNJI Kota"},{"@language":"ja","@value":"郡司 滉大"}],"jpcoar:affiliationName":[{"@language":"ja","@value":"中央大学大学院 理工学研究科 都市人間環境学専攻"}]},{"@id":"https://cir.nii.ac.jp/crid/1410289940732681090","@type":"Researcher","personIdentifier":[{"@type":"NRID","@value":"9000414252034"}],"foaf:name":[{"@language":"en","@value":"MIYAUCHI Toshiharu"},{"@language":"ja","@value":"宮内 俊晴"}],"jpcoar:affiliationName":[{"@language":"ja","@value":"中央大学大学院 理工学研究科 都市人間環境学専攻"}]},{"@id":"https://cir.nii.ac.jp/crid/1420001326219987968","@type":"Researcher","personIdentifier":[{"@type":"KAKEN_RESEARCHERS","@value":"30847190"},{"@type":"NRID","@value":"1000030847190"},{"@type":"NRID","@value":"9000411218749"},{"@type":"NRID","@value":"9000310334501"},{"@type":"NRID","@value":"9000414252482"},{"@type":"NRID","@value":"9000410656209"},{"@type":"NRID","@value":"9000414251954"},{"@type":"NRID","@value":"9000326263008"},{"@type":"NRID","@value":"9000410655574"},{"@type":"NRID","@value":"9000414252177"},{"@type":"NRID","@value":"9000414252035"},{"@type":"NRID","@value":"9000410655807"},{"@type":"NRID","@value":"9000410655246"},{"@type":"NRID","@value":"9000414252423"},{"@type":"NRID","@value":"9000414252041"},{"@type":"NRID","@value":"9000410655845"},{"@type":"NRID","@value":"9000414251745"},{"@type":"NRID","@value":"9000414252249"},{"@type":"NRID","@value":"9000410655014"},{"@type":"NRID","@value":"9000410655509"},{"@type":"NRID","@value":"9000414252160"},{"@type":"NRID","@value":"9000414252264"},{"@type":"NRID","@value":"9000410655447"},{"@type":"NRID","@value":"9000414223038"},{"@type":"RESEARCHMAP","@value":"https://researchmap.jp/masa0703"}],"foaf:name":[{"@language":"en","@value":"WATANABE Masashi"},{"@language":"ja","@value":"渡部 真史"}],"jpcoar:affiliationName":[{"@language":"ja","@value":"中央大学 理工学部都市環境学科"}]},{"@id":"https://cir.nii.ac.jp/crid/1410289940732681088","@type":"Researcher","personIdentifier":[{"@type":"NRID","@value":"9000414252036"}],"foaf:name":[{"@language":"en","@value":"ARIKAWA Taro"},{"@language":"ja","@value":"有川 太郎"}],"jpcoar:affiliationName":[{"@language":"ja","@value":"中央大学 理工学部都市環境学科"}]}],"publication":{"publicationIdentifier":[{"@type":"EISSN","@value":"18838944"},{"@type":"PISSN","@value":"18842399"}],"prism:publicationName":[{"@language":"en","@value":"Journal of Japan Society of Civil Engineers, Ser. B2 (Coastal Engineering)"},{"@language":"ja","@value":"土木学会論文集Ｂ２（海岸工学）"},{"@value":"土木学会論文集B2(海岸工学)"},{"@language":"en","@value":"Journal of Japan Society of Civil Engineers, Ser. B2 (Coastal Engineering)"},{"@language":"ja","@value":"海岸工学論文集"}],"dc:publisher":[{"@language":"en","@value":"Japan Society of Civil Engineers"},{"@language":"ja","@value":"公益社団法人 土木学会"}],"prism:publicationDate":"2021","prism:volume":"77","prism:number":"2","prism:startingPage":"I_307","prism:endingPage":"I_312"},"reviewed":"false","url":[{"@id":"https://www.jstage.jst.go.jp/article/kaigan/77/2/77_I_307/_pdf"}],"availableAt":"2021","foaf:topic":[{"@id":"https://cir.nii.ac.jp/all?q=Machine%20learning","dc:title":"Machine learning"},{"@id":"https://cir.nii.ac.jp/all?q=Neural%20network","dc:title":"Neural network"},{"@id":"https://cir.nii.ac.jp/all?q=Tsunami%20arrival%20time","dc:title":"Tsunami arrival time"},{"@id":"https://cir.nii.ac.jp/all?q=Tsunami%20simulation","dc:title":"Tsunami simulation"}],"relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1360855569708759040","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Rapid prediction of alongshore run-up distribution from near-field tsunamis"}]},{"@id":"https://cir.nii.ac.jp/crid/1390282679528570112","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["references"],"jpcoar:relatedTitle":[{"@language":"en","@value":"Real-time Prediction of Tsunami Propagating into the Osaka Bay by Using Artificial Neural Network"},{"@value":"ニューラルネットワークを用いた大阪湾内への来襲津波のリアルタイム予測に関する研究"}]},{"@id":"https://cir.nii.ac.jp/crid/1390282752347419136","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["references"],"jpcoar:relatedTitle":[{"@language":"en","@value":"Wave Prediction in the Sea of Japan by Deep Learning Using Meteorological Data"}]},{"@id":"https://cir.nii.ac.jp/crid/1390587675161457280","@type":"Article","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@language":"ja","@value":"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"},{"@language":"en","@value":"CONSTRUCTION OF A TSUNAMI WATER LEVEL ESTIMATION MODEL BASED ON FAULT PARAMETERS USING DEEP LEARNING"}]}],"dataSourceIdentifier":[{"@type":"JALC","@value":"oai:japanlinkcenter.org:2008926942"},{"@type":"CROSSREF","@value":"10.2208/kaigan.77.2_i_307"},{"@type":"CIA","@value":"130008113344"},{"@type":"CROSSREF","@value":"10.2208/jscejj.25-17001_references_DOI_VLhcrZ43IznYTu34a4ZRrLYuALr"}]}