Modeling of atmospheric particulate matters via artificial intelligence methods

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dc.authorscopusid56539994200
dc.authorscopusid57223041118
dc.authorscopusid55400713200
dc.contributor.authorCihan, Pınar
dc.contributor.authorÖzel, Hüseyin
dc.contributor.authorÖzcan, Hüseyin Kurtuluş
dc.date.accessioned2022-05-11T14:15:57Z
dc.date.available2022-05-11T14:15:57Z
dc.date.issued2021
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractNowadays, pollutants continue to be released into the atmosphere in increasing amounts with each passing day. Some of them may turn into more harmful forms by accumulating in different layers of the atmosphere at different times and can be transported to other regions with atmospheric events. Particulate matter (PM) is one of the most important air pollutants in the atmosphere, and it can be released into the atmosphere by natural and anthropogenic processes or can be formed in the atmosphere as a result of chemical reactions. In this study, it was aimed to predict PM10 and PM2.5 components measured in an industrial zone selected by adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), classification and regression trees (CART), random forest (RF), k-nearest neighbor (KNN), and extreme learning machine (ELM) methods. To this end, in the first stage of the study, the dataset consisting of air pollutants and meteorological data was created, the temporal and qualitative evaluation of these data was performed, and the PM (PM10 and PM2.5) components were modeled using the R software environment by artificial intelligence methods. The ANFIS model was more successful in predicting the PM10 (R-2 = 0.95, RMSE = 5.87, MAE = 4.75) and PM2.5 (R-2 = 0.97, RMSE = 3.05, MAE = 2.18) values in comparison with other methods. As a result of the study, it was clearly observed that the ANFIS model could be used in the prediction of air pollutants.
dc.description.sponsorshipIstanbul University-Cerrahpasa Graduate Education Institute
dc.description.sponsorshipThe authors are thankful to Istanbul University-Cerrahpasa Graduate Education Institute for support.
dc.identifier.doi10.1007/s10661-021-09091-1
dc.identifier.issn0167-6369
dc.identifier.issn1573-2959
dc.identifier.issue5en_US
dc.identifier.pmid33884498
dc.identifier.scopus2-s2.0-85104610161
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s10661-021-09091-1
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6133
dc.identifier.volume193
dc.identifier.wosWOS:000642147800001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorCihan, Pınar
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofEnvironmental Monitoring and Assessment
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectParticular matter
dc.subjectMeteorological data
dc.subjectArtificial intelligence
dc.subjectMachine learning
dc.subjectAir-Pollution
dc.subjectAnfis
dc.titleModeling of atmospheric particulate matters via artificial intelligence methods
dc.typeArticle

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