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dc.contributor.authorAlver Şahin, Ülküen_US
dc.contributor.authorUçan, Osman Nurien_US
dc.contributor.authorBayat, Cumaen_US
dc.contributor.authorTolluoğlu, Orhanen_US
dc.date.accessioned2016-06-02T10:15:09Z
dc.date.available2016-06-02T10:15:09Z
dc.date.issued2011
dc.identifier.citationAlver Şahin, Ü., Uçan, O.N., Bayat, C., Tolluoğlu, O. (2011). A new approach to prediction of SO2 and PM10 concentrations in Istanbul, Turkey: Cellular Neural Network (CNN). Environmental Forensics. 12, 253–269.en_US
dc.identifier.issn1527-5922
dc.identifier.issn1527-5930
dc.identifier.urihttp://hdl.handle.net/20.500.12294/473
dc.identifier.urihttp://dx.doi.org/10.1080/15275922.2011.595047
dc.descriptionBayat, Cuma (Arel Author)en_US
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.en_US
dc.language.isoengen_US
dc.publisherTaylor & Francisen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCellular Neural Network (CNN)en_US
dc.subjectAir Pollutionen_US
dc.subjectParticulate Matter (PM)en_US
dc.subjectSulfur Dioxide (SO2)en_US
dc.subjectMeteorologyen_US
dc.titleA new approach to prediction of SO2 and PM10 concentrations in Istanbul, Turkey: Cellular Neural Network (CNN)en_US
dc.typearticleen_US
dc.relation.journalEnvironmental Forensicsen_US
dc.contributor.departmentİstanbul Arel Üniversitesi, Mühendislik ve Mimarlık Fakültesi, Endüstri Mühendisliği Bölümü.en_US
dc.contributor.authorIDTR176409en_US
dc.contributor.authorIDTR26113en_US
dc.contributor.authorIDTR45123en_US
dc.identifier.volume12en_US
dc.identifier.startpage253en_US
dc.identifier.endpage269en_US
dc.relation.publicationcategoryUluslararası Hakemli Dergien_US


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