Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/200
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dc.contributor.authorAyturan, Y.A.en_US
dc.contributor.authorAyturan, Zeynep Cansuen_US
dc.contributor.authorAltun, H.O.en_US
dc.contributor.authorKongoli, C.en_US
dc.contributor.authorTuncez, F.D.en_US
dc.contributor.authorDursun, Şükrüen_US
dc.contributor.authorÖztürk. Alien_US
dc.date.accessioned2021-12-13T10:19:58Z-
dc.date.available2021-12-13T10:19:58Z-
dc.date.issued2020en_US
dc.identifier.issn1790-7632-
dc.identifier.urihttps://doi.org/10.30955/gnj.003208-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/200-
dc.description.abstractParticulate matter (PM), classified according to aerodynamic diameter, is one of the harmful pollutants causing health damaging effects. It is considered as cancerogenic by the World Health Organization (WHO) because of the substances found in the chemical composition of PM. In this study, short-term prediction of PM2.5 pollution at 1, 2 and 3 hours was modelled using deep learning methods. Three deep learning algorithms and the combination thereof were evaluated: Long-short term memory units (LSTM), recurrent neural networks (RNN) and gated recurrent unit (GRU). Air Quality Monitoring Stations of the Ministry of Environment and Urbanization of Turkey were utilized to obtain the data. Specifically, meteorological and air pollution data were obtained from a monitoring station located in Kecioren District of Ankara. Several trials were conducted using different combinations of RNN, GRU and LSTM models. Pollutant concentrations and meteorological factors were integrated into the model as input parameters to predict PM2.5 concentration for 1, 2 and 3 hours. Best results with R-2 of 0.83, 0.7 and 0.63 for 1, 2-, and 3-hour predictions, respectively, were obtained by using a combination of GRU and RNN models. The results of this study are promising for explaining the effect of different deep learning models on prediction performance.en_US
dc.language.isoenen_US
dc.publisherGLOBAL NETWORK ENVIRONMENTAL SCIENCE & TECHNOLOGYen_US
dc.relation.ispartofGLOBAL NEST JOURNALen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAir pollutionen_US
dc.subjectparticulate matteren_US
dc.subjectdeep learningen_US
dc.subjectpredictionen_US
dc.subjectGRUen_US
dc.subjectRNNen_US
dc.titleShort-term prediction of PM2.5 pollution with deep learning methodsen_US
dc.typeArticleen_US
dc.identifier.doi10.30955/gnj.003208-
dc.identifier.scopus2-s2.0-85085090892en_US
dc.contributor.affiliationFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Çevre Mühendisliği Bölümüen_US
dc.contributor.affiliationFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Çevre Mühendisliği Bölümüen_US
dc.relation.issn1790-7632en_US
dc.coverage.doihttps://doi.org/10.30955/gnj.003208en_US
dc.description.volume22en_US
dc.description.issue1en_US
dc.description.startpage126en_US
dc.description.endpage131en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.authorid0000-0001-9502-1178en_US
dc.authorid0000-0001-9513-4949en_US
dc.authorwosidTuncez, Fatma Didem/ABI-7129-2020-
dc.authorwosidDURSUN, SUKRU/A-8432-2019-
dc.authorwosidOzturk, Ali/AAG-3039-2021-
dc.authorwosidDURSUN, SUKRU/A-5579-2018-
dc.authorwosidAyturan, Zeynep Cansu/A-1288-2019-
dc.authorwosidAyturan, Yasin Akin/R-6620-2018-
dc.identifier.volume22en_US
dc.identifier.issue1en_US
dc.identifier.startpage126en_US
dc.identifier.endpage131en_US
dc.identifier.wosWOS:000523568400016en_US
dc.institutionauthorDursun, Şükrüen_US
dc.institutionauthorAyturan, Zeynep Cansuen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.authorscopusid57216886929-
dc.authorscopusid57204971317-
dc.authorscopusid57212227954-
dc.authorscopusid6602760623-
dc.authorscopusid57196260173-
dc.authorscopusid7005761090-
dc.authorscopusid57197223283-
dc.identifier.scopusqualityQ3-
item.openairetypeArticle-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.dept02.06. Department of Environmental Engineering-
crisitem.author.dept02.06. Department of Environmental Engineering-
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections
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