Dissolved oxygen estimation using artificial neural network for water quality control

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dc.authorid0000-0003-1630-5290en_US
dc.authorid0000-0002-5326-3491
dc.authorid0000-0001-5077-6518
dc.authorid0000-0002-4820-5123
dc.contributor.authorŞengörür, Bülent
dc.contributor.authorDoğan, Emrah
dc.contributor.authorKöklü, Rabia
dc.contributor.authorSamandar, Ayhan
dc.date.accessioned2021-06-23T19:19:32Z
dc.date.available2021-06-23T19:19:32Z
dc.date.issued2006
dc.departmentBAİBÜ, Rektörlük, Diğer Yayınlaren_US
dc.description13th International Symposium on Environmental Pollution and Its Impact on Life in the Mediterranean Region -- OCT 08-12, 2005 -- Thessaloniki, GREECEen_US
dc.description.abstractDissolved oxygen (DO) is one of the key parameters when analyzing river water quality. Correct estimation of DO being carried by a river is very important for water quality control. DO is affected by lots of variables such as decomposition, nitrification, reaeration, sedimentation, photosynthesis, water discharge and temperature for that reason it is hard to solve such a complex problem. The methods available in the literature for DO estimation are complicated, time consuming and necessitate numbersome parameter estimation procedures. Artificial Neural Networks (ANNs) are simply mathematical representations of the functioning of the human brain. This paper examines the potential of ANN in estimating the DO from limited data (NO2-N, NO3-N, BOD, water discharge and temperature). This study employed feed forward (FF) type ANN for computing monthly values of DO. The results of the study clearly demonstrate that the ANN results are very close to the observed values of DO.en_US
dc.description.sponsorshipMediterranean Sci Assoc Environm Protecten_US
dc.identifier.endpage1072en_US
dc.identifier.issn1018-4619
dc.identifier.issn1610-2304
dc.identifier.issue9Aen_US
dc.identifier.scopus2-s2.0-33749656875en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1069en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12491/5959
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-33749656875&partnerID=40&md5=d81b08527eb877263cdec2642edc51f7
dc.identifier.volume15en_US
dc.identifier.wosWOS:000241082400014en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorSamandar, Ayhan
dc.language.isoenen_US
dc.publisherParlar Scientific Publications (P S P)en_US
dc.relation.ispartofFresenius Environmental Bulletinen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networken_US
dc.subjectDissolved Oxygenen_US
dc.subjectWater Qualityen_US
dc.titleDissolved oxygen estimation using artificial neural network for water quality controlen_US
dc.typeConference Objecten_US

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