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dc.contributor.authorTolluoglu, Orhan
dc.contributor.authorBayat, Cuma
dc.contributor.authorSahin, Ulku Alver
dc.contributor.authorUÇAN, OSMAN NURİ
dc.date.accessioned2021-03-03T13:39:15Z
dc.date.available2021-03-03T13:39:15Z
dc.date.issued2011
dc.identifier.citationSahin U. A. , UÇAN O. N. , Bayat C., Tolluoglu O., "A New Approach to Prediction of SO2 and PM10 Concentrations in Istanbul, Turkey: Cellular Neural Network (CNN)", ENVIRONMENTAL FORENSICS, cilt.12, sa.3, ss.253-269, 2011
dc.identifier.issn1527-5922
dc.identifier.otherav_3546a2d4-a757-4260-94bf-5a48ab7709ea
dc.identifier.othervv_1032021
dc.identifier.urihttp://hdl.handle.net/20.500.12627/40010
dc.identifier.urihttps://doi.org/10.1080/15275922.2011.595047
dc.description.abstractThis article describes the application of a cellular neural network (CNN) to model air pollutants. In this study, forthcoming daily and hourly values of particulate matter (PM10) and sulphur dioxide (SO2) were predicted. These air pollutant concentrations were measured at four different locations (Yenibosna, Sarachane, Umraniye and Kadikoy) in Istanbul between 2002 and 2003. Eight different meteorological parameters (temperature, wind speed and direction, humidity, pressure, sunshine, cloudiness, rainfall) recorded at Florya and Goztepe meteorological stations were used to model inputs. First, the results of CNN prediction and statistical persistence method (PER) were compared. Then, CNN and PER outputs were correlated with real time values by using statistical performance indices. The indices of agreement (d) for daily mean concentrations were found using CNN and PER prediction models: 0.71-0.80 and 0.71-0.78 for PM10, and 0.81-0.84 and 0.77-0.82 for SO2 in all air quality measurement stations, respectively. From these values, CNN prediction model are concluded to be more accurate than PER, which is used for comparison. In hourly prediction of mean concentrations with CNN, d value is found as 0.78 and 0.92 for PM10 and SO2, respectively. Thus, it was concluded that CNN-based approaches could be promising for air pollutant prediction.
dc.language.isoeng
dc.subjectTarım ve Çevre Bilimleri (AGE)
dc.subjectÇevre Mühendisliği
dc.subjectÇEVRE BİLİMLERİ
dc.subjectÇevre / Ekoloji
dc.subjectTarımsal Bilimler
dc.subjectMühendislik ve Teknoloji
dc.titleA New Approach to Prediction of SO2 and PM10 Concentrations in Istanbul, Turkey: Cellular Neural Network (CNN)
dc.typeMakale
dc.relation.journalENVIRONMENTAL FORENSICS
dc.contributor.departmentİstanbul Aydın Üniversitesi , ,
dc.identifier.volume12
dc.identifier.issue3
dc.identifier.startpage253
dc.identifier.endpage269
dc.contributor.firstauthorID199081


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