Usage of Output-Dependent Data Scaling in Modeling and Prediction of Air Pollution Daily Concentration Values (PM10) in the City of Konya

Basit Öğe Kaydı
dc.contributor.authorPolat, Kemal
dc.contributor.authorDurduran, S. Savaş
dc.date.accessioned2020-03-26T18:32:04Z
dc.date.available2020-03-26T18:32:04Z
dc.date.issued2012
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThis paper presents the combination of a data preprocessing called output-dependent data scaling (ODDS) and adaptive network-based fuzzy inference system (ANFIS) to predict the air pollution daily levels including particulate matter (PM10) concentration values belonging to the city of Konya in Turkey. Also, we have used the regression models including least square regression, partial least square regression, and multivariate linear regression as prediction models in addition to ANFIS model. Data transformation or normalization methods should be used to increase the performance of used prediction models and are used prior to prediction algorithms. In this study, we have used the output-dependent data scaling method as data transformation method and combined it with ANFIS and regression models. PM10 concentration dataset has been taken from Air Quality Statistics database of Turkish Statistical Institute. In PM10 concentration dataset, the mean values belonging to seasons of winter period have been used with the aim of watching the air pollution changes between dates of December, 1, 2003 and December, 30, 2005 in the city of Konya. In the forecasting of PM10 concentration in Konya province, temperature (A degrees C), humidity (%), pressure (kPa), and wind velocity (km/h) attributes have been used. The experimental results demonstrated that the ODDS method has obtained very promising results in the prediction of PM10 concentration values.en_US
dc.identifier.citationDurduran, S. S., Polat, K., (2012). Usage of Output-Dependent Data Scaling in Modeling and Prediction of Air Pollution Daily Concentration Values (PM10) in the City of Konya. Neural Computing & Applications, 21(8), 2153-2162. Doi:10.1007/s00521-011-0661-z
dc.identifier.doi10.1007/s00521-011-0661-zen_US
dc.identifier.endpage2162en_US
dc.identifier.issn0941-0643en_US
dc.identifier.issn1433-3058en_US
dc.identifier.issue8en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage2153en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s00521-011-0661-z
dc.identifier.urihttps://hdl.handle.net/20.500.12395/28607
dc.identifier.volume21en_US
dc.identifier.wosWOS:000309878400035en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorDurduran, S. Savaş
dc.language.isoenen_US
dc.publisherSPRINGER LONDON LTDen_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectOutput-dependent data scalingen_US
dc.subjectPM10en_US
dc.subjectPredictionen_US
dc.subjectAdaptive network-based fuzzy inference systemen_US
dc.subjectLinear regression modelsen_US
dc.subjectAir pollutionen_US
dc.titleUsage of Output-Dependent Data Scaling in Modeling and Prediction of Air Pollution Daily Concentration Values (PM10) in the City of Konyaen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
8607.pdf
Boyut:
777.01 KB
Biçim:
Adobe Portable Document Format
Açıklama:
Makale Dosyası
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Harita Mühendisliği Bölümü/Makale Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu