Cellular automata modeling approaches to forecast urban growth for adana, Turkey: A comparative approach

Basit Öğe Kaydı
dc.authorid0000-0001-5267-9105en_US
dc.contributor.authorBerberoglu, Suha
dc.contributor.authorAkın Tanrıöver, Anıl
dc.contributor.authorClarke, Keith C.
dc.date.accessioned2021-03-20T20:14:29Z
dc.date.available2021-03-20T20:14:29Z
dc.date.issued2016
dc.departmentBTÜ, Orman Fakültesi, Peyzaj Mimarlığı Bölümüen_US
dc.description.abstractThe aim of this study was to assess the application of cellular automata in urban modeling to give insights into a wide variety of urban phenomena, using the most commonly used urban modeling approaches including: Markov Chain, SLEUTH, Dinamica EGO modelling with the Logistic Regression (LR), Regression Tree (RT) and Artificial Neural Networks (ANN). The effectiveness of these approaches in forecasting the urban growth was assessed in the example of Adana as a fast growing City in Turkey for the year 2023. Different models have their own merits and advantages, the empirical results and findings of various approaches provided a guide for urban sprawl modeling. The accuracy figures to assess the models were derived using Allocation and Disagreement maps together with Kappa statistics. Calibration data were from remotely sensed images recorded in 1967, 1977, 1987, 1998 and 2007. SLEUTH, Markov Chain and RT models resulted in overall Kappa accuracy measures of 75%, 72% and 71% respectively, measured over the past data using hindcasting. LR and ANN yielded the least accurate results with an overall Kappa accuracy of 66%. Different modeling approaches have their own merits. However, the SLEUTH model was the most accurate for handling the variability in the present urban development. (C) 2016 Elsevier B.V. All rights reserved.en_US
dc.description.sponsorshipTUBITAK (The Scientific and Tecnhnological Research Council of Turkey), TurkeyTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK); Cukurova University, Scientific Research ProjectsCukurova University [ZF2009D13]en_US
dc.description.sponsorshipThe authors would like to thank TUBITAK (The Scientific and Tecnhnological Research Council of Turkey), Turkey and Cukurova University, Scientific Research Projects (Project no: ZF2009D13) for their financial support. The authors would like to thank TUBITAK (The Scientific and Tecnhnological Research Council of Turkey), Turkey for financial support.en_US
dc.identifier.doi10.1016/j.landurbplan.2016.04.017en_US
dc.identifier.endpage27en_US
dc.identifier.issn0169-2046
dc.identifier.issn1872-6062
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage11en_US
dc.identifier.urihttp://doi.org/10.1016/j.landurbplan.2016.04.017
dc.identifier.urihttps://hdl.handle.net/20.500.12885/1060
dc.identifier.volume153en_US
dc.identifier.wosWOS:000379705700002en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAkın Tanrıöver, Anıl
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofLandscape And Urban Planningen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectUrban modelingen_US
dc.subjectSLEUTHen_US
dc.subjectLogistic regressionen_US
dc.subjectRegression treeen_US
dc.titleCellular automata modeling approaches to forecast urban growth for adana, Turkey: A comparative approachen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Peyzaj Mimarlığı Bölümü Yayın Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu