dc.creator | Morales Esteban, Antonio | es |
dc.creator | Martínez Álvarez, F. | es |
dc.creator | Troncoso Lora, Alicia | es |
dc.creator | Justo, J. L. | es |
dc.creator | Rubio Escudero, Cristina | es |
dc.date.accessioned | 2022-12-01T10:58:38Z | |
dc.date.available | 2022-12-01T10:58:38Z | |
dc.date.issued | 2010 | |
dc.identifier.citation | Morales Esteban, A., Martínez Álvarez, F., Troncoso Lora, A., Justo, J.L. y Rubio Escudero, C. (2010). Pattern recognition to forecast seismic time series. Expert Systems with Applications, 37 (12), 8333-8342. https://doi.org/10.1016/j.eswa.2010.05.050. | |
dc.identifier.issn | 0957-4174 | es |
dc.identifier.uri | https://hdl.handle.net/11441/140002 | |
dc.description.abstract | Earthquakes arrive without previous warning and can destroy a whole city in a few seconds, causing numerous deaths and
economical losses. Nowadays, a great effort is being made to develop techniques that forecast these unpredictable natural
disasters in order to take precautionary measures. In this paper, clustering techniques are used to obtain patterns which
model the behavior of seismic temporal data and can help to predict medium–large earthquakes. First, earthquakes are
classified into different groups and the optimal number of groups, a priori unknown, is determined. Then, patterns are
discovered when medium–large earthquakes happen. Results from the Spanish seismic temporal data provided by the
Spanish Geographical Institute and non-parametric statistical tests are presented and discussed, showing a remarkable
performance and the significance of the obtained results. | es |
dc.description.sponsorship | Ministerio de Ciencia y Tecnología BIA2004-01302 | es |
dc.description.sponsorship | Ministerio de Ciencia y Tecnología TIN-68084-C02 | es |
dc.description.sponsorship | Junta de Andalucía P07-TIC-02611 | es |
dc.format | application/pdf | es |
dc.format.extent | 10 | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Expert Systems with Applications, 37 (12), 8333-8342. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Time series | es |
dc.subject | Earthquakes forecasting | es |
dc.subject | Clustering | es |
dc.title | Pattern recognition to forecast seismic time series | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Estructuras de Edificación e Ingeniería del Terreno | es |
dc.relation.projectID | BIA2004-01302 | es |
dc.relation.projectID | TIN-68084-C02 | es |
dc.relation.projectID | P07-TIC-02611 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0957417410004616?via%3Dihub | es |
dc.identifier.doi | 10.1016/j.eswa.2010.05.050 | es |
dc.contributor.group | Universidad de Sevilla. TIC-254: Data Science and Big Data Lab | es |
dc.journaltitle | Expert Systems with Applications | es |
dc.publication.volumen | 37 | es |
dc.publication.issue | 12 | es |
dc.publication.initialPage | 8333 | es |
dc.publication.endPage | 8342 | es |
dc.contributor.funder | Ministerio de Ciencia Y Tecnología (MCYT). España | es |
dc.contributor.funder | Junta de Andalucía | es |