Support vector machines for urban growth modeling
Publication in refereed journal

香港中文大學研究人員

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替代計量分析
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摘要This paper presents a novel method to model urban land use conversion using support vector machines (SVMs), a new generation of machine learning algorithms used in the classification and regression domains. This method derives the relationship between rural-urban land use change and various factors, such as population, distance to road and facilities, and surrounding land use. Our study showed that SVMs are an effective approach to estimating the land use conversion model, owing to their ability to model non-linear relationships, good generalization performance, and achievement of a global and unique optimum. The rural-urban land use conversions of New Castle County, Delaware between 1984-1992, 1992-1997, and 1997-2002 were used as a case study to demonstrate the applicability of SVMs to urban expansion modeling. The performance of SVMs was also compared with a commonly used binomial logistic regression (BLR) model, and the results, in terms of the overall modeling accuracy and McNamara's test, consistently corroborated the better performance of SVMs.
著者Huang B, Xie CL, Tay R
期刊名稱GeoInformatica
出版年份2010
月份1
日期1
卷號14
期次1
出版社SPRINGER
頁次83 - 99
國際標準期刊號1384-6175
電子國際標準期刊號1573-7624
語言英式英語
關鍵詞Logistic regression; Support vector machines; Urban growth
Web of Science 學科類別Computer Science; Computer Science, Information Systems; COMPUTER SCIENCE, INFORMATION SYSTEMS; Geography, Physical; GEOGRAPHY, PHYSICAL; Physical Geography

上次更新時間 2020-17-11 於 01:47