Spatial prediction of PM10 concentration using machine learning algorithms in Ankara, Turkey

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dc.authoridDokuz, Yesim/0000-0001-7202-2899
dc.contributor.authorBozdag, Asli
dc.contributor.authorDokuz, Yesim
dc.contributor.authorGokcek, Oznur Begum
dc.date.accessioned2024-11-07T13:24:13Z
dc.date.available2024-11-07T13:24:13Z
dc.date.issued2020
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractWith the increase in population and industrialization, air pollution has become one of the global problems nowadays. Therefore, air pollutant parameters should be measured at regular intervals, and the necessary measures should be taken by evaluating the results of measurements. In order to prevent air pollution, pollutant parameters must be evaluated within the framework of a model. Recently, in order to obtain objective and more sensitive results with regard to air pollution nowadays, studies, which use machine learning algorithms in artificial intelligence technologies, have been carried out. In this study, PM10 concentrations, which are obtained from 7 stations in Ankara province in Turkey, were trained with machine learning algorithms (LASSO, SVR, RF, kNN, xGBoost, ANN). The PM10 concentrations of the years 2009-2017 of 6 stations in Ankara were given as input, and the PM10 concentrations of the seventh station for the year 2018 were predicted. The model development stage was repeated for each station, and the performance and error rates of the algorithms were determined by comparing the results produced by the algorithms with the actual results. The best results were provided with ANN (R-2 = 0.58, RMSE = 20.8, MAE = 14.4). The spatial distribution of the estimated concentration results was provided through Geographic Information System (GIS), and spatial strategies for improving air pollution over land use were established. (c) 2020 Elsevier Ltd. All rights reserved.
dc.identifier.doi10.1016/j.envpol.2020.114635
dc.identifier.issn0269-7491
dc.identifier.issn1873-6424
dc.identifier.pmid33618491
dc.identifier.scopus2-s2.0-85083825455
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.envpol.2020.114635
dc.identifier.urihttps://hdl.handle.net/11480/13981
dc.identifier.volume263
dc.identifier.wosWOS:000539426400133
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherElsevier Sci Ltd
dc.relation.ispartofEnvironmental Pollution
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectAir pollution parameter
dc.subjectMachine learning
dc.subjectSpatial distribution
dc.subjectArtificial intelligence
dc.subjectPredictive modeling
dc.titleSpatial prediction of PM10 concentration using machine learning algorithms in Ankara, Turkey
dc.typeArticle

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