Ranking water quality variables using feature selection algorithms to improve generalization capability of artificial neural networks

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dc.contributor.authorŞamandar, Ayhan
dc.date.accessioned2020-04-30T23:31:33Z
dc.date.available2020-04-30T23:31:33Z
dc.date.issued2010
dc.departmentDÜ, Düzce Meslek Yüksekokulu, İnşaat Bölümüen_US
dc.descriptionWOS: 000279559800005en_US
dc.description.abstractArtificial neural networks (ANNs) have been recently used intensively in the water resource management and planning. As the demand on the clean water increases, the estimation of important parameters using ANNs to evaluate the surface water quality has become an importance practice. In this study, feature selection methods, which are important tools in data mining, are used to shed light into a further understanding in the correlation between measured parameters of water quality variables and dissolved oxygen level. The aim is to enhance the generalization ability of ANNs in prediction of dissolved oxygen level. Three different feature selection methods are utilized to discern the inherent correlation in the water quality variables in a given data set collected from Melen River, Turkey. Results show that, a small set of variable can be identified, which effectively improves the generalization ability of ANNs.en_US
dc.identifier.endpage1259en_US
dc.identifier.issn1992-2248
dc.identifier.issue11en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1254en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12684/4305
dc.identifier.volume5en_US
dc.identifier.wosWOS:000279559800005en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherAcademic Journalsen_US
dc.relation.ispartofScientific Research And Essaysen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectWater qualityen_US
dc.subjectartificial neural networksen_US
dc.subjectMelen riveren_US
dc.subjectrankingen_US
dc.titleRanking water quality variables using feature selection algorithms to improve generalization capability of artificial neural networksen_US
dc.typeArticleen_US

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