GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China

doi:10.1016/j.geomorph.2011.12.040

	GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China
	Xu, Chong 1,2; Dai, Fuchu 2; Xu, Xiwei 1; Lee, Yuan Hsi 3
刊名	GEOMORPHOLOGY
	2012-04-01
卷号	145 页码:70-80
关键词	Wenchuan earthquake Landslides SVM GIS Landslide susceptibility mapping
ISSN号	0169-555X
DOI	10.1016/j.geomorph.2011.12.040
文献子类	Article
英文摘要	Support vector machine (SVM) modeling is based on statistical learning theory. It involves a training phase with associated input and target output values. In recent years, the method has become increasingly popular. The main purpose of this study is to evaluate the mapping power of SVM modeling in earthquake triggered landslide-susceptibility mapping for a section of the Jianjiang River watershed using a Geographic Information System (GIS) software. The river was affected by the Wenchuan earthquake of May 12, 2008. Visual interpretation of colored aerial photographs of 1-m resolution and extensive field surveys provided a detailed landslide inventory map containing 3147 landslides related to the 2008 Wenchuan earthquake. Elevation, slope angle, slope aspect, distance from seismogenic faults, distance from drainages, and lithology were used as the controlling parameters. For modeling, three groups of positive and negative training samples were used in concert with four different kernel functions. Positive training samples include the centroids of 500 large landslides, those of all 3147 landslides, and 5000 randomly selected points in landslide polygons. Negative training samples include 500, 3147, and 5000 randomly selected points on slopes that remained stable during the Wenchuan earthquake. The four kernel functions are linear, polynomial, radial basis, and sigmoid. In total, 12 cases of landslide susceptibility were mapped. Comparative analyses of landslide-susceptibility probability and area relation curves show that both the polynomial and radial basis functions suitably classified the input data as either landslide positive or negative though the radial basis function was more successful. The 12 generated landslide-susceptibility maps were compared with known landslide centroid locations and landslide polygons to verify the success rate and predictive accuracy of each model. The 12 results were further validated using area-under-curve analysis. Group 3 with 5000 randomly selected points on the landslide polygons, and 5000 randomly selected points along stable slopes gave the best results with a success rate of 79.20% and predictive accuracy of 79.13% under the radial basis function. Of all the results, the sigmoid kernel function was the least skillful when used in concert with the centroid data of all 3147 landslides as positive training samples, and the negative training samples of 3147 randomly selected points in regions of stable slope (success rate = 54.95%; predictive accuracy = 61.85%). This paper also provides suggestions and reference data for selecting appropriate training samples and kernel function types for earthquake triggered landslide-susceptibility mapping using SVM modeling. Predictive landslide-susceptibility maps could be useful in hazard mitigation by helping planners understand the probability of landslides in different regions. (C) 2011 Elsevier B.V. All rights reserved.
WOS关键词	WENCHUAN EARTHQUAKE ; HAZARD EVALUATION ; HONG-KONG ; PREDICTION ; TERRAIN
WOS研究方向	Physical Geography ; Geology
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000301475400007
资助机构	International Scientific Joint Project of China(2009DFA21280) ; International Scientific Joint Project of China(2009DFA21280) ; International Scientific Joint Project of China(2009DFA21280) ; International Scientific Joint Project of China(2009DFA21280) ; National Science Foundation of China(40821160550) ; National Science Foundation of China(40821160550) ; National Science Foundation of China(40821160550) ; National Science Foundation of China(40821160550) ; Doctoral Candidate Innovation Research Support Program by Science & Technology Review(kjdb200902-5) ; Doctoral Candidate Innovation Research Support Program by Science & Technology Review(kjdb200902-5) ; Doctoral Candidate Innovation Research Support Program by Science & Technology Review(kjdb200902-5) ; Doctoral Candidate Innovation Research Support Program by Science & Technology Review(kjdb200902-5) ; International Scientific Joint Project of China(2009DFA21280) ; International Scientific Joint Project of China(2009DFA21280) ; International Scientific Joint Project of China(2009DFA21280) ; International Scientific Joint Project of China(2009DFA21280) ; National Science Foundation of China(40821160550) ; National Science Foundation of China(40821160550) ; National Science Foundation of China(40821160550) ; National Science Foundation of China(40821160550) ; Doctoral Candidate Innovation Research Support Program by Science & Technology Review(kjdb200902-5) ; Doctoral Candidate Innovation Research Support Program by Science & Technology Review(kjdb200902-5) ; Doctoral Candidate Innovation Research Support Program by Science & Technology Review(kjdb200902-5) ; Doctoral Candidate Innovation Research Support Program by Science & Technology Review(kjdb200902-5) ; International Scientific Joint Project of China(2009DFA21280) ; International Scientific Joint Project of China(2009DFA21280) ; International Scientific Joint Project of China(2009DFA21280) ; International Scientific Joint Project of China(2009DFA21280) ; National Science Foundation of China(40821160550) ; National Science Foundation of China(40821160550) ; National Science Foundation of China(40821160550) ; National Science Foundation of China(40821160550) ; Doctoral Candidate Innovation Research Support Program by Science & Technology Review(kjdb200902-5) ; Doctoral Candidate Innovation Research Support Program by Science & Technology Review(kjdb200902-5) ; Doctoral Candidate Innovation Research Support Program by Science & Technology Review(kjdb200902-5) ; Doctoral Candidate Innovation Research Support Program by Science & Technology Review(kjdb200902-5) ; International Scientific Joint Project of China(2009DFA21280) ; International Scientific Joint Project of China(2009DFA21280) ; International Scientific Joint Project of China(2009DFA21280) ; International Scientific Joint Project of China(2009DFA21280) ; National Science Foundation of China(40821160550) ; National Science Foundation of China(40821160550) ; National Science Foundation of China(40821160550) ; National Science Foundation of China(40821160550) ; Doctoral Candidate Innovation Research Support Program by Science & Technology Review(kjdb200902-5) ; Doctoral Candidate Innovation Research Support Program by Science & Technology Review(kjdb200902-5) ; Doctoral Candidate Innovation Research Support Program by Science & Technology Review(kjdb200902-5) ; Doctoral Candidate Innovation Research Support Program by Science & Technology Review(kjdb200902-5)
内容类型	期刊论文
源URL	[http://ir.iggcas.ac.cn/handle/132A11/84652]
专题	中国科学院地质与地球物理研究所
通讯作者	Xu, Xiwei
作者单位	1.China Earthquake Adm, Inst Geol, Key Lab Act Tecton & Volcano, Beijing 100029, Peoples R China 2.Chinese Acad Sci, Inst Geol & Geophys, Beijing 100029, Peoples R China 3.Natl Chung Cheng Univ, Dept Earth & Environm Sci, Chiayi 62102, Taiwan
推荐引用方式 GB/T 7714	Xu, Chong,Dai, Fuchu,Xu, Xiwei,et al. GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China[J]. GEOMORPHOLOGY,2012,145:70-80.
APA	Xu, Chong,Dai, Fuchu,Xu, Xiwei,&Lee, Yuan Hsi.(2012).GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China.GEOMORPHOLOGY,145,70-80.
MLA	Xu, Chong,et al."GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China".GEOMORPHOLOGY 145(2012):70-80.