Modeling of trophospheric ozone concentrations using genetically trained multi-level cellular neural networks

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dc.contributor.authorOzcan, H. Kurtulus
dc.contributor.authorBilgil, Erdem
dc.contributor.authorSahin, Ulku
dc.contributor.authorUcan, O. Nuri
dc.contributor.authorBayat, Cuma
dc.date.accessioned2024-03-13T10:30:42Z
dc.date.available2024-03-13T10:30:42Z
dc.date.issued2007
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractTropospheric ozone concentrations, which are an important air pollutant, are modeled by the use of an artificial intelligence structure. Data obtained from air pollution measurement stations in the city of Istanbul are utilized in constituting the model. A supervised algorithm for the evaluation of ozone concentration using a genetically trained multi-level cellular neural network (ML-CNN) is introduced, developed, and applied to real data. A genetic algorithm is used in the optimization of CNN templates. The model results and the actual measurement results are compared and statistically evaluated. It is observed that seasonal changes in ozone concentrations are reflected effectively by the concentrations estimated by the multilevel-CNN model structure, with a correlation value of 0.57 ascertained between actual and model results. It is shown that the multilevel-CNN modeling technique is as satisfactory as other modeling techniques in associating the data in a complex medium in air pollution applications.en_US
dc.identifier.doi10.1007/s00376-007-0907-y
dc.identifier.endpage914en_US
dc.identifier.issn0256-1530
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-38149102312en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage907en_US
dc.identifier.urihttps://doi.org/10.1007/s00376-007-0907-y
dc.identifier.urihttps://hdl.handle.net/20.500.12662/3501
dc.identifier.volume24en_US
dc.identifier.wosWOS:000249569100015en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherScience China Pressen_US
dc.relation.ispartofAdvances In Atmospheric Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectgenetic algorithmen_US
dc.subjectcellular neural networks (CNN)en_US
dc.subjectozoneen_US
dc.subjectmeteorological dataen_US
dc.titleModeling of trophospheric ozone concentrations using genetically trained multi-level cellular neural networksen_US
dc.typeArticleen_US

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