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dc.creatorSamardžić-Petrović, Mileva
dc.creatorDragićević, Suzana
dc.creatorKovačević, Miloš
dc.creatorBajat, Branislav
dc.date.accessioned2019-04-19T14:25:46Z
dc.date.available2019-04-19T14:25:46Z
dc.date.issued2016
dc.identifier.issn1361-1682
dc.identifier.urihttp://grafar.grf.bg.ac.rs/handle/123456789/777
dc.description.abstractSupport Vector Machines (SVM) is a machine learning (ML) algorithm commonly applied to the classification of remotely sensing data and more recently for modeling land use changes. However, in most geospatial applications the current literature does not elaborate on specifications of the SVM method with respect to data sampling, attribute selection and optimal parameters choices. Therefore the main objective of this study is to present and investigate the SVM technique for modeling urban land use change. The SVM model building procedure is presented together with the detailed evaluation of the output results with respect to the choice of datasets, attributes and the change of SVM parameters. Geospatial datasets containing nine land use classes and spatial attributes for the Municipality of Zemun, Republic of Serbia were used for years 2001, 2003, 2007 and 2011. The Correlation-based Feature Subset method, kappa coefficient, Area Under Receiver Operating Characteristic Curve (AUC) and kappa simulation were used to perform the model evaluation and compare the model outputs with the real land use datasets. The obtained results indicate that the SVM-based models perform better when implementing balanced data sampling, reduced data sets to informative subsets of attributes and properly identify the optimal learning parameters.en
dc.publisherBlackwell Publishing Ltd
dc.relationinfo:eu-repo/grantAgreement/MESTD/Integrated and Interdisciplinary Research (IIR or III)/47014/RS//
dc.relationNatural Sciences and Engineering Research Council (NSERC) of Canada 328224-2012
dc.rightsrestrictedAccess
dc.sourceTransactions in Gis
dc.titleModeling Urban Land Use Changes Using Support Vector Machinesen
dc.typearticle
dc.rights.licenseARR
dc.citation.epage734
dc.citation.issue5
dc.citation.other20(5): 718-734
dc.citation.rankM21
dc.citation.spage718
dc.citation.volume20
dc.identifier.doi10.1111/tgis.12174
dc.identifier.rcubconv_1836
dc.identifier.scopus2-s2.0-84991512343
dc.identifier.wos000385267600005
dc.type.versionpublishedVersion


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