ORCID Profile
0000-0002-5063-3522
Current Organisation
The Chinese University of Hong Kong
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Publisher: Informa UK Limited
Date: 02-09-2015
Publisher: Informa UK Limited
Date: 15-01-2014
Publisher: Elsevier BV
Date: 02-2016
Publisher: SAGE Publications
Date: 2009
DOI: 10.1068/B33047
Abstract: Modeling 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.
Publisher: Informa UK Limited
Date: 12-12-2022
Publisher: Springer Science and Business Media LLC
Date: 25-02-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2012
Publisher: Informa UK Limited
Date: 12-2011
No related grants have been discovered for Bo Huang.