dc.creator | Martínez Álvarez, Francisco | es |
dc.creator | Troncoso Lora, Alicia | es |
dc.creator | Morales Esteban, Antonio | es |
dc.creator | Riquelme Santos, José Cristóbal | es |
dc.date.accessioned | 2016-06-15T09:17:43Z | |
dc.date.available | 2016-06-15T09:17:43Z | |
dc.date.issued | 2011 | |
dc.identifier.isbn | 978-3-642-21221-5 | es |
dc.identifier.issn | 0302-9743 | es |
dc.identifier.uri | http://hdl.handle.net/11441/42288 | |
dc.description.abstract | Nowadays, much effort is being devoted to develop techniques
that forecast natural disasters in order to take precautionary
measures. In this paper, the extraction of quantitative association rules
and regression techniques are used to discover patterns which model the
behavior of seismic temporal data to help in earthquakes prediction.
Thus, a simple method based on the k–smallest and k–greatest values
is introduced for mining rules that attempt at explaining the conditions
under which an earthquake may happen. On the other hand patterns are
discovered by using a tree-based piecewise linear model. Results from
seismic temporal data provided by the Spanish’s Geographical Institute
are presented and discussed, showing a remarkable performance and the
significance of the obtained results. | es |
dc.description.sponsorship | Ministerio de Ciencia y tecnología TIN2007-68084-C-02 | |
dc.description.sponsorship | Junta de Andalucía P07-TIC-02611 | |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | Hybrid Artificial Intelligent Systems : 6th International Conference, HAIS 2011, Wroclaw, Poland, May 23-25, 2011, Proceedings, Part II. Lecture Notes in Computer Science, v.6679 | es |
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 | quantitative association rules | es |
dc.subject | regression | es |
dc.title | Computational Intelligence Techniques for Predicting Earthquakes | es |
dc.type | info:eu-repo/semantics/bookPart | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/acceptedVersion | 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 | TIN2007-68084-C-02 | es |
dc.relation.projectID | P07-TIC-02611 | es |
dc.identifier.doi | http://dx.doi.org/10.1007/978-3-642-21222-2_35 | es |
idus.format.extent | 8 | es |
dc.publication.initialPage | 287 | es |
dc.publication.endPage | 294 | es |
dc.relation.publicationplace | Berlin | es |
dc.identifier.idus | https://idus.us.es/xmlui/handle/11441/42288 | |
dc.contributor.funder | Ministerio de Ciencia y Tecnología (MCYT). España | |
dc.contributor.funder | Junta de Andalucía | |