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dc.creatorMartínez Ballesteros, María del Mares
dc.creatorSalcedo Sanz, S.es
dc.creatorRiquelme Santos, José Cristóbales
dc.creatorCasanova Mateo, C.es
dc.creatorCamacho, J. L.es
dc.date.accessioned2022-04-26T09:33:55Z
dc.date.available2022-04-26T09:33:55Z
dc.date.issued2011
dc.identifier.citationMartínez Ballesteros, M.d.M., Salcedo Sanz, S., Riquelme Santos, J.C., Casanova Mateo, C. y Camacho, J.L. (2011). Evolutionary association rules for total ozone content modeling from satellite observations. Chemometrics and Intelligent Laboratory Systems, 109 (2), 217-227.
dc.identifier.issn0169-7439es
dc.identifier.urihttps://hdl.handle.net/11441/132621
dc.description.abstractIn this paper we propose an evolutionary method of association rules discovery (EQAR, Evolutionary Quan titative Association Rules) that extends a recently published algorithm by the authors and we describe its ap plication to a problem of Total Ozone Content (TOC) modeling in the Iberian Peninsula. We use TOC data from the Total Ozone Mapping Spectrometer (TOMS) on board the NASA Nimbus-7 satellite measured at three lo cations (Lisbon, Madrid and Murcia) of the Iberian Peninsula. As prediction variables for the association rules we consider several meteorological variables, such as Outgoing Long-wave Radiation (OLR), Temperature at 50 hPa level, Tropopause height, and wind vertical velocity component at 200 hPa. We show that the best as sociation rules obtained by EQAR are able to accurate modeling the TOC data in the three locations consid ered, providing results which agree to previous works in the literaturees
dc.formatapplication/pdfes
dc.format.extent11es
dc.language.isoenges
dc.publisherElsevieres
dc.relation.ispartofChemometrics and Intelligent Laboratory Systems, 109 (2), 217-227.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAssociation Ruleses
dc.subjectEvolutionary algorithmses
dc.subjectTotal Ozone Content (TOC)es
dc.subjectSatellite dataes
dc.titleEvolutionary association rules for total ozone content modeling from satellite observationses
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticoses
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0169743911002012es
dc.identifier.doi10.1016/j.chemolab.2011.09.011es
dc.contributor.groupUniversidad de Sevilla. TIC-254: Data Science and Big Data Labes
dc.journaltitleChemometrics and Intelligent Laboratory Systemses
dc.publication.volumen109es
dc.publication.issue2es
dc.publication.initialPage217es
dc.publication.endPage227es
dc.identifier.sisius20101556es

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