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Article: Land-use-change modeling using unbalanced support-vector machines

TitleLand-use-change modeling using unbalanced support-vector machines
Authors
Issue Date2009
Citation
Environment and Planning B: Planning and Design, 2009, v. 36, n. 3, p. 398-416 How to Cite?
AbstractModeling land-use change is a prerequisite to understanding the complexity of land-use-change patterns. This paper presents a novel method to model urban land-use change using support-vector machines (SVMs), a new generation of machine learning algorithms used in classification and regression domains. An SVM modeling framework has been developed to analyze land-use change in relation to various factors such as population, distance to roads and facilities, and surrounding land use. As land-use data are generally unbalanced, in the sense that the unchanged data overwhelm the changed data, traditional methods are incapable of classifying relatively minor land-use changes with high accuracy. To circumvent this problem, an unbalanced SVM has been adopted by enhancing the standard SVMs. A case study of Calgary land-use change demonstrates that the unbalanced SVMs can achieve high and reliable performance for land-use-change modeling. © 2008 Pion Ltd and its Licensors.
Persistent Identifierhttp://hdl.handle.net/10722/330117
ISSN
2016 Impact Factor: 1.527
2019 SCImago Journal Rankings: 1.109
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHuang, Bo-
dc.contributor.authorXie, Chenglin-
dc.contributor.authorTay, Richard-
dc.contributor.authorWu, Bo-
dc.date.accessioned2023-08-09T03:37:54Z-
dc.date.available2023-08-09T03:37:54Z-
dc.date.issued2009-
dc.identifier.citationEnvironment and Planning B: Planning and Design, 2009, v. 36, n. 3, p. 398-416-
dc.identifier.issn0265-8135-
dc.identifier.urihttp://hdl.handle.net/10722/330117-
dc.description.abstractModeling land-use change is a prerequisite to understanding the complexity of land-use-change patterns. This paper presents a novel method to model urban land-use change using support-vector machines (SVMs), a new generation of machine learning algorithms used in classification and regression domains. An SVM modeling framework has been developed to analyze land-use change in relation to various factors such as population, distance to roads and facilities, and surrounding land use. As land-use data are generally unbalanced, in the sense that the unchanged data overwhelm the changed data, traditional methods are incapable of classifying relatively minor land-use changes with high accuracy. To circumvent this problem, an unbalanced SVM has been adopted by enhancing the standard SVMs. A case study of Calgary land-use change demonstrates that the unbalanced SVMs can achieve high and reliable performance for land-use-change modeling. © 2008 Pion Ltd and its Licensors.-
dc.languageeng-
dc.relation.ispartofEnvironment and Planning B: Planning and Design-
dc.titleLand-use-change modeling using unbalanced support-vector machines-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1068/b33047-
dc.identifier.scopuseid_2-s2.0-66349131248-
dc.identifier.volume36-
dc.identifier.issue3-
dc.identifier.spage398-
dc.identifier.epage416-
dc.identifier.eissn1472-3417-
dc.identifier.isiWOS:000266856100003-

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