Artificial neural network for tsunami forecasting
This paper presents a data-driven approach for effective and efficient forecasting of tsunami generated by underwater earthquakes. Based on pre-computed tsunami scenarios as training data sets the Artificial Neural Network (ANN) is used for the construction of data-driven forecasting models. The tra...
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| Veröffentlicht in: | Journal of Asian earth sciences 2009-09, Vol.36 (1), p.29-37 |
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| container_title | Journal of Asian earth sciences |
| container_volume | 36 |
| creator | Romano, Michele Liong, Shie-Yui Vu, Minh Tue Zemskyy, Pavlo Doan, Chi Dung Dao, My Ha Tkalich, Pavel |
| description | This paper presents a data-driven approach for effective and efficient forecasting of tsunami generated by underwater earthquakes. Based on pre-computed tsunami scenarios as training data sets the Artificial Neural Network (ANN) is used for the construction of data-driven forecasting models. The training data comprised spatial values of maximum tsunami heights and tsunami arrival times (snapshots), computed with process-based TUNAMI-N2-NUS model for the most probable ocean floor rupture scenarios. Validation tests demonstrated that with a given earthquake size and location, the ANN method provides accurate and near instantaneous forecasting of the maximum tsunami heights and arrival times for the entire computational domain. |
| doi_str_mv | 10.1016/j.jseaes.2008.11.003 |
| format | Article |
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Validation tests demonstrated that with a given earthquake size and location, the ANN method provides accurate and near instantaneous forecasting of the maximum tsunami heights and arrival times for the entire computational domain.</description><subject>Artificial neural network</subject><subject>Data-driven model</subject><subject>Marine</subject><subject>Tsunami forecast</subject><issn>1367-9120</issn><issn>1878-5786</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><recordid>eNqFkM1OwzAQhC0EEqXwBhx64pawG6eOfUGqKv6kSlzgbDnOGjmkSbFTEG-PSzjDaecwM5r9GLtEyBFQXLd5G8lQzAsAmSPmAPyIzVBWMltWUhwnzUWVKSzglJ3F2AKAkCXMGK7C6J233nSLnvbh54yfQ3hbuCEsxrjvzdYfNFkTR9-_nrMTZ7pIF793zl7ubp_XD9nm6f5xvdpkhiscs8ZJqYRUXFUk0KB1oilqqciqtM4pKhrBeVnKurCyqCQ6XgHWBkhUNaoln7OrqXcXhvc9xVFvfbTUdaanYR81L4WEgpf_GgtIIDhCMpaT0YYhxkBO74LfmvClEfQBpG71BFIfQGpEnaam2M0Uo_Tth6ego_XUW2p8gjLqZvB_F3wDZoR8xw</recordid><startdate>20090904</startdate><enddate>20090904</enddate><creator>Romano, Michele</creator><creator>Liong, Shie-Yui</creator><creator>Vu, Minh Tue</creator><creator>Zemskyy, Pavlo</creator><creator>Doan, Chi Dung</creator><creator>Dao, My Ha</creator><creator>Tkalich, Pavel</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>F1W</scope><scope>H96</scope><scope>L.G</scope><scope>7QQ</scope><scope>8FD</scope><scope>FR3</scope><scope>JG9</scope><scope>KR7</scope></search><sort><creationdate>20090904</creationdate><title>Artificial neural network for tsunami forecasting</title><author>Romano, Michele ; Liong, Shie-Yui ; Vu, Minh Tue ; Zemskyy, Pavlo ; Doan, Chi Dung ; Dao, My Ha ; Tkalich, Pavel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a391t-df889689397e61a1cf6d2b89ec9003f9e2d633448b2c82781f3701ba0e67b1953</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Artificial neural network</topic><topic>Data-driven model</topic><topic>Marine</topic><topic>Tsunami forecast</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Romano, Michele</creatorcontrib><creatorcontrib>Liong, Shie-Yui</creatorcontrib><creatorcontrib>Vu, Minh Tue</creatorcontrib><creatorcontrib>Zemskyy, Pavlo</creatorcontrib><creatorcontrib>Doan, Chi Dung</creatorcontrib><creatorcontrib>Dao, My Ha</creatorcontrib><creatorcontrib>Tkalich, Pavel</creatorcontrib><collection>CrossRef</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Ceramic Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Materials Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>Journal of Asian earth sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Romano, Michele</au><au>Liong, Shie-Yui</au><au>Vu, Minh Tue</au><au>Zemskyy, Pavlo</au><au>Doan, Chi Dung</au><au>Dao, My Ha</au><au>Tkalich, Pavel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial neural network for tsunami forecasting</atitle><jtitle>Journal of Asian earth sciences</jtitle><date>2009-09-04</date><risdate>2009</risdate><volume>36</volume><issue>1</issue><spage>29</spage><epage>37</epage><pages>29-37</pages><issn>1367-9120</issn><eissn>1878-5786</eissn><abstract>This paper presents a data-driven approach for effective and efficient forecasting of tsunami generated by underwater earthquakes. 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| issn | 1367-9120 1878-5786 |
| language | eng |
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| source | Elsevier ScienceDirect Journals Complete |
| subjects | Artificial neural network Data-driven model Marine Tsunami forecast |
| title | Artificial neural network for tsunami forecasting |
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