{"created":"2023-07-27T06:25:49.309480+00:00","id":9277,"links":{},"metadata":{"_buckets":{"deposit":"a1165822-7cb2-4f90-aca4-1dfcd24d1ed3"},"_deposit":{"created_by":3,"id":"9277","owners":[3],"pid":{"revision_id":0,"type":"depid","value":"9277"},"status":"published"},"_oai":{"id":"oai:kanazawa-u.repo.nii.ac.jp:00009277","sets":["934:935:936"]},"author_link":["13311","1037","257"],"item_4_biblio_info_8":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2016-11-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicPageEnd":"I_180","bibliographicPageStart":"I_175","bibliographicVolumeNumber":"72","bibliographic_titles":[{"bibliographic_title":"土木学会論文集B2(海岸工学) = Journal of Japan Society of Civil Engineers, Ser. B2 (Coastal Engineering)"}]}]},"item_4_description_21":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"本研究では，日本海沿岸域，特に富山湾を含む周辺で発生し，これまでも甚大な被害を引き起こしているうねり性の高波「寄り回り波」について，ニューラルネットワークを用いてその発生を予測可能とするモデルの構築を試みる．ニューラルネットモデルの構築に際しては，寄り回り波の発生メカニズムを参考にうねり性の高波の発生源となる東北以北の日本海域での気象および海象データを入力因子とし，対象とする波浪観測地点の観測波高を出力因子とする． 　解析の結果，うねり性の高波の発生源における大気圧，風速成分および波高を入力因子とした場合，13時間程度経過後における対象地点での波高を良好に再現できることが明らかになった． Long swell prediction around Japan Sea is examined using artificial neural network. In this artificial neural network, meteorological data around the generation point of long swell is adopted as input data, and wave data of prediction point is used as output data. As a result, it is found that atmospheric pressure and velocity are suitable for the factor of input data, and the occurrence of long swell at prediction point is possible to estimate using half a day before meteorological data as input data.","subitem_description_type":"Abstract"}]},"item_4_publisher_17":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"土木学会 = Japan Society of Civil Engineers"}]},"item_4_relation_12":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type":"isIdenticalTo","subitem_relation_type_id":{"subitem_relation_type_id_text":"10.2208/kaigan.72.I_175","subitem_relation_type_select":"DOI"}}]},"item_4_relation_28":{"attribute_name":"関連URI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"http://www.jsce.or.jp/","subitem_relation_type_select":"URI"}},{"subitem_relation_type_id":{"subitem_relation_type_id_text":"https://www.jstage.jst.go.jp/article/kaigan/72/2/72_I_1543/_article/-char/ja/","subitem_relation_type_select":"URI"}}]},"item_4_rights_23":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":"Copyright © 2016 by Japan Society of Civil Engineers 土木学会"}]},"item_4_source_id_11":{"attribute_name":"NCID","attribute_value_mlt":[{"subitem_source_identifier":"AA12508551","subitem_source_identifier_type":"NCID"}]},"item_4_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1884-2399","subitem_source_identifier_type":"ISSN"}]},"item_4_version_type_25":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"斎藤, 武久"}],"nameIdentifiers":[{},{},{},{}]},{"creatorNames":[{"creatorName":"小久保, 元貴"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"間瀬, 肇"}],"nameIdentifiers":[{},{},{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2017-10-03"}],"displaytype":"detail","filename":"TE-PR-SAITOH-T-175.pdf","filesize":[{"value":"1.5 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"TE-PR-SAITOH-T-175.pdf","url":"https://kanazawa-u.repo.nii.ac.jp/record/9277/files/TE-PR-SAITOH-T-175.pdf"},"version_id":"fe60c917-18ab-491f-972f-25f5108aac11"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"ニューラルネットワークを用いた日本海沿岸域でのうねり性高波浪の予測に関する研究","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ニューラルネットワークを用いた日本海沿岸域でのうねり性高波浪の予測に関する研究"},{"subitem_title":"Long swell prediction around Japan Sea using artifical neural network","subitem_title_language":"en"}]},"item_type_id":"4","owner":"3","path":["936"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-10-03"},"publish_date":"2017-10-03","publish_status":"0","recid":"9277","relation_version_is_last":true,"title":["ニューラルネットワークを用いた日本海沿岸域でのうねり性高波浪の予測に関する研究"],"weko_creator_id":"3","weko_shared_id":-1},"updated":"2023-07-28T01:56:46.240552+00:00"}