Cihan, PınarOzcan, Husayin KurtulusÖngen, Atakan2024-10-292024-10-2920232149-49162149-9373https://doi.org/10.30855/gmbd.0705087https://search.trdizin.gov.tr/tr/yayin/detay/1228482https://hdl.handle.net/20.500.11776/13051Human 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.en10.30855/gmbd.0705087info:eu-repo/semantics/openAccessArtificial intelligencebaggingmultilayer perceptronurban environmentozoneArticle935575731228482