Prediction of tropospheric ozone concentration with Bagging-MLP method

dc.contributor.authorCihan, Pınar
dc.contributor.authorOzcan, Husayin Kurtulus
dc.contributor.authorÖngen, Atakan
dc.date.accessioned2024-10-29T17:50:32Z
dc.date.available2024-10-29T17:50:32Z
dc.date.issued2023
dc.departmentTekirdağ Namık Kemal Üniversitesien_US
dc.description.abstractHuman activities are linked to atmospheric pollution and are affected by economic development. Ground-level ozone has become an important and harmful pollutant for many countries, adversely affecting public health. As there is a limited number of on-site measurements, alternative methods are required to accurately estimate ozone concentrations. In this study, a database containing annual average concentrations of CO2, N2O, CO, NOx, SOx, and O3, covering the years 2008-2018 for ten countries in Europe, was created. Ten different artificial intelligence regression methods were developed to predict the O3 concentration using these variables. The predictive performance of the developed artificial intelligence models was compared using the coefficient of determination, mean absolute error, root mean square error, and relative absolute error criteria. Experimental results show that the Bagging-MLP method has a better prediction performance than other models in ozone concentration estimation, with an R2 value of 0.9994, mean absolute error of 24.67, root mean square error of 33.85, and relative absolute error of 2.9%. This study shows that the O3 concentration can be estimated very close to the actual value by using the Bagging-MLP method, one of the artificial intelligence methods.en_US
dc.identifier.doi10.30855/gmbd.0705087
dc.identifier.endpage573en_US
dc.identifier.issn2149-4916
dc.identifier.issn2149-9373
dc.identifier.issue3en_US
dc.identifier.startpage557en_US
dc.identifier.trdizinid1228482en_US
dc.identifier.urihttps://doi.org/10.30855/gmbd.0705087
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1228482
dc.identifier.urihttps://hdl.handle.net/20.500.11776/13051
dc.identifier.volume9en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofGazi Mühendislik Bilimleri Dergisien_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial intelligenceen_US
dc.subjectbaggingen_US
dc.subjectmultilayer perceptronen_US
dc.subjecturban environmenten_US
dc.subjectozoneen_US
dc.titlePrediction of tropospheric ozone concentration with Bagging-MLP methoden_US
dc.typeArticleen_US

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