Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China

doi:10.1016/j.geomorph.2008.02.011

	Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China
	Yao, X.1; Tham, L. G.2; Dai, F. C.1
刊名	GEOMORPHOLOGY
	2008-11-01
卷号	101 期号:4 页码:572-582
关键词	Landslide susceptibility mapping Support Vector Machine (SVM) One-class sample Two-class sample Logistic regression method Hong Kong
ISSN号	0169-555X
DOI	10.1016/j.geomorph.2008.02.011
文献子类	Article
英文摘要	The Support Vector Machine (SVM) is an increasingly popular learning procedure based on statistical learning theory, and involves a training phase in which the model is trained by a training dataset of associated input and target output values. The trained model is then used to evaluate a separate set of testing data. There are two main ideas underlying the SVM for discriminant-type problems. The first is an optimum linear separating hyperplane that separates the data patterns. The second is the use of kernel functions to convert the original non-linear data patterns into the format that is linearly separable in a high-dimensional feature space. In this paper, an overview of the SVM, both one-class and two-class SVM methods, is first presented followed by its use in landslide susceptibility mapping. A study area was selected from the natural terrain of Hong Kong, and slope angle, slope aspect, elevation, profile curvature of slope, lithology, vegetation cover and topographic wetness index (TWI) were used as environmental parameters which influence the occurrence of landslides. One-class and two-class SVM models were trained and then used to map landslide susceptibility respectively. The resulting susceptibility maps obtained by the methods were compared to that obtained by the logistic regression (LR) method. It is concluded that two-class SVM possesses better prediction efficiency than logistic regression and one-class SVM. However, one-class SVM, which only requires failed cases, has an advantage over the other two methods as only "failed" case information is usually available in landslide susceptibility mapping. (C) 2008 Elsevier B.V. All rights reserved.
WOS关键词	ARTIFICIAL NEURAL-NETWORKS ; LOGISTIC-REGRESSION ; STATISTICAL-MODELS ; GIS ; HAZARD ; CLASSIFICATION ; GENERATION ; TURKEY ; ISLAND ; JAPAN
WOS研究方向	Physical Geography ; Geology
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000260897100004
资助机构	Research Grants Council(HKU 7176/05E) ; Research Grants Council(HKU 7176/05E) ; Research Grants Council(HKU 7176/05E) ; Research Grants Council(HKU 7176/05E) ; Research Grants Council(HKU 7176/05E) ; Research Grants Council(HKU 7176/05E) ; Research Grants Council(HKU 7176/05E) ; Research Grants Council(HKU 7176/05E) ; Research Grants Council(HKU 7176/05E) ; Research Grants Council(HKU 7176/05E) ; Research Grants Council(HKU 7176/05E) ; Research Grants Council(HKU 7176/05E) ; Research Grants Council(HKU 7176/05E) ; Research Grants Council(HKU 7176/05E) ; Research Grants Council(HKU 7176/05E) ; Research Grants Council(HKU 7176/05E)
内容类型	期刊论文
源URL	[http://ir.iggcas.ac.cn/handle/132A11/69720]
专题	中国科学院地质与地球物理研究所
通讯作者	Yao, X.
作者单位	1.Chinese Acad Sci, Inst Geol & Geophys, Beijing 100029, Peoples R China 2.Univ Hong Kong, Dept Civil Engn, Hong Kong, Hong Kong, Peoples R China
推荐引用方式 GB/T 7714	Yao, X.,Tham, L. G.,Dai, F. C.. Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China[J]. GEOMORPHOLOGY,2008,101(4):572-582.
APA	Yao, X.,Tham, L. G.,&Dai, F. C..(2008).Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China.GEOMORPHOLOGY,101(4),572-582.
MLA	Yao, X.,et al."Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China".GEOMORPHOLOGY 101.4(2008):572-582.