Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China

doi:10.1016/j.cageo.2012.01.002

	Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China
	Xu, Chong 1,2; Xu, Xiwei 1; Dai, Fuchu 2; Saraf, Arun K.3
刊名	COMPUTERS & GEOSCIENCES
	2012-09-01
卷号	46 页码:317-329
关键词	Earthquake triggered landslides Landslide susceptibility mapping Bivariate statistics Logistic regression Artificial neural networks Support vector machine
ISSN号	0098-3004
DOI	10.1016/j.cageo.2012.01.002
文献子类	Article
英文摘要	The main purpose of this study is to compare the following six GIS-based models for susceptibility mapping of earthquake triggered landslides: bivariate statistics (BS), logistic regression (LR), artificial neural networks (ANN), and three types of support vector machine (SVM) models that use the three different kernel functions linear, polynomial, and radial basis. The models are applied in a tributary watershed of the Fu River, a tributary of the Jialing River, which is part of the area of China affected by the May 12, 2008 Wenchuan earthquake. For this purpose, eleven thematic data layers are used: landslide inventory, slope angle, aspect, elevation, curvature, distance from drainages, topographic wetness index (TWI), distance from main roads, distance from surface rupture, peak ground acceleration (PGA), and lithology. The data layers were specifically constructed for analysis in this study. In the subsequent stage of the study, susceptibility maps were produced using the six models and the same input for each one. The validations of the resulting susceptibility maps were performed and compared by means of two values of area under curve (AUC) that represent the respective success rates and prediction rates. The AUC values obtained from all six results showed that the LR model provides the highest success rate (AUC = 80.34) and the highest prediction rate (AUC = 80.27). The SVM (radial basis function) model generates the second-highest success rate (AUC = 80.302) and the second-highest prediction rate (AUC = 80.151), which are close to the value from the LR model. The results using the SVM (linear) model show the lowest AUC values. The AUC values from the SVM (linear) model are only 72.52 (success rates) and 72.533 (prediction rates). Furthermore, the results also show that the radial basis function is the most appropriate kernel function of the three kernel functions applied using the SVM model for susceptibility mapping of earthquake triggered landslides in the study area. The paper also provides a counter-example for the widely held notion that validation performances of the results from application of the models obtained from soft computing techniques (such as ANN and SVM) are higher than those from applications of LR and BA models. (C) 2012 Elsevier Ltd. All rights reserved.
WOS关键词	ARTIFICIAL NEURAL-NETWORKS ; SUPPORT VECTOR MACHINE ; LOGISTIC-REGRESSION ; CONDITIONAL-PROBABILITY ; BIVARIATE STATISTICS ; SAMPLING STRATEGIES ; PREDICTION MODELS ; HONG-KONG ; GIS ; TURKEY
WOS研究方向	Computer Science ; Geology
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:000307924200036
资助机构	National Science Foundation of China(40821160550 ; National Science Foundation of China(40821160550 ; National Science Foundation of China(40821160550 ; National Science Foundation of China(40821160550 ; 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) ; 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) ; 40974057) ; 40974057) ; 40974057) ; 40974057) ; National Science Foundation of China(40821160550 ; National Science Foundation of China(40821160550 ; National Science Foundation of China(40821160550 ; National Science Foundation of China(40821160550 ; 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) ; 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) ; 40974057) ; 40974057) ; 40974057) ; 40974057) ; National Science Foundation of China(40821160550 ; National Science Foundation of China(40821160550 ; National Science Foundation of China(40821160550 ; National Science Foundation of China(40821160550 ; 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) ; 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) ; 40974057) ; 40974057) ; 40974057) ; 40974057) ; National Science Foundation of China(40821160550 ; National Science Foundation of China(40821160550 ; National Science Foundation of China(40821160550 ; National Science Foundation of China(40821160550 ; 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) ; 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) ; 40974057) ; 40974057) ; 40974057) ; 40974057)
内容类型	期刊论文
源URL	[http://ir.iggcas.ac.cn/handle/132A11/84326]
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.Indian Inst Technol Roorkee, Dept Earth Sci, Roorkee 247667, Uttar Pradesh, India
推荐引用方式 GB/T 7714	Xu, Chong,Xu, Xiwei,Dai, Fuchu,et al. Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China[J]. COMPUTERS & GEOSCIENCES,2012,46:317-329.
APA	Xu, Chong,Xu, Xiwei,Dai, Fuchu,&Saraf, Arun K..(2012).Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China.COMPUTERS & GEOSCIENCES,46,317-329.
MLA	Xu, Chong,et al."Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China".COMPUTERS & GEOSCIENCES 46(2012):317-329.