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dc.contributor.authorArroyo Puente, Ángel 
dc.contributor.authorHerrero Cosío, Álvaro 
dc.contributor.authorTricio Gómez, Verónica 
dc.contributor.authorCorchado, Emilio 
dc.contributor.authorWoźniak, Michał
dc.date.accessioned2023-01-18T07:59:26Z
dc.date.available2023-01-18T07:59:26Z
dc.date.issued2018
dc.identifier.issn1076-2787
dc.identifier.urihttp://hdl.handle.net/10259/7261
dc.description.abstractOzone is one of the pollutants with most negative efects on human health and in general on the biosphere. Many data-acquisition networks collect data about ozone values in both urban and background areas. Usually, these data are incomplete or corrupt and the imputation of the missing values is a priority in order to obtain complete datasets, solving the uncertainty and vagueness of existing problems to manage complexity. In the present paper, multiple-regression techniques and Artifcial Neural Network models are applied to approximate the absent ozone values from fve explanatory variables containing air-quality information. To compare the diferent imputation methods, real-life data from six data-acquisition stations from the region of Castilla y Leon (Spain) are gathered ´ in diferent ways and then analyzed. Te results obtained in the estimation of the missing values by applying these techniques and models are compared, analyzing the possible causes of the given response.en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherHindawies
dc.relation.ispartofComplexity. 2018, V. 2018, p. 1-14es
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.titleNeural Models for Imputation of Missing Ozone Data in Air-Quality Datasetsen
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.1155/2018/7238015es
dc.identifier.doi10.1155/2018/7238015
dc.identifier.essn1099-0526
dc.journal.titleComplexityen
dc.volume.number2018es
dc.page.initial1es
dc.page.final14es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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