Validation of Spatial Prediction Models for Landslide Susceptibility Mapping by Considering Structural Similarity

doi:10.3390/ijgi6040103

CORC > 地质与地球物理研究所 > 中国科学院地质与地球物理研究所 > 中国科学院页岩气与地质工程重点实验室

	Validation of Spatial Prediction Models for Landslide Susceptibility Mapping by Considering Structural Similarity
	Deng, Xiaolong 1,2; Li, Lihui 1,2; Tan, Yufang1,2
刊名	ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
	2017-04-01
卷号	6 期号:4
关键词	geographic information system (GIS) landslide frequency ratio certainty factor validation structural similarity index
ISSN号	2220-9964
DOI	10.3390/ijgi6040103
文献子类	Article
英文摘要	In this paper, we propose a methodology for validating landslide susceptibility results in the Pinggu district (Beijing, China). A landslide inventory including 169 landslides was prepared, and eight factors correlated to landslides (lithology, tectonic faults, topographic elevation, slope gradient, aspect, slope curvature, land use, and road network) were processed, integrating two techniques, namely the frequency ratio (FR) and the certainty factor (CF), in a geographic information system (GIS) environment. The area under the curve (success rate curve and prediction curve) analysis was used to evaluate model compatibility and predictability. Validation results indicated that the values of the area under the curve for the FR model and the CF model were 0.769 and 0.768, respectively. Considering spatial correlation, an alternative complementary method for validating landslide susceptibility maps was introduced. The spatially approximate maps could be discriminated from their matrices which carry structural information, and the structural similarity index (SSI) was then proposed to quantify the similarity. As a specific example, the SSI value of the FR (74.15%) scored higher than that of the CF model (69.36%), demonstrating its promise in validating different landslide susceptibility maps. These results show that the FR model outperforms the CF model in producing a landslide susceptibility map in the study area.
WOS关键词	LOGISTIC-REGRESSION MODELS ; FREQUENCY RATIO ; CERTAINTY FACTOR ; DEMPSTER-SHAFER ; GIS ; HAZARD ; WEIGHTS ; BASIN ; RISK ; MAPS
WOS研究方向	Physical Geography ; Remote Sensing
语种	英语
出版者	MDPI AG
WOS记录号	WOS:000404525000012
资助机构	National Natural Science Foundation of China(41372322 ; National Natural Science Foundation of China(41372322 ; Key National Natural Science Foundation of China(41330643) ; Key National Natural Science Foundation of China(41330643) ; 41172269) ; 41172269) ; National Natural Science Foundation of China(41372322 ; National Natural Science Foundation of China(41372322 ; Key National Natural Science Foundation of China(41330643) ; Key National Natural Science Foundation of China(41330643) ; 41172269) ; 41172269) ; National Natural Science Foundation of China(41372322 ; National Natural Science Foundation of China(41372322 ; Key National Natural Science Foundation of China(41330643) ; Key National Natural Science Foundation of China(41330643) ; 41172269) ; 41172269) ; National Natural Science Foundation of China(41372322 ; National Natural Science Foundation of China(41372322 ; Key National Natural Science Foundation of China(41330643) ; Key National Natural Science Foundation of China(41330643) ; 41172269) ; 41172269)
内容类型	期刊论文
源URL	[http://ir.iggcas.ac.cn/handle/132A11/49277]
专题	地质与地球物理研究所_中国科学院页岩气与地质工程重点实验室
通讯作者	Li, Lihui
作者单位	1.Chinese Acad Sci, Inst Geol & Geophys, Beijing 100029, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Deng, Xiaolong,Li, Lihui,Tan, Yufang. Validation of Spatial Prediction Models for Landslide Susceptibility Mapping by Considering Structural Similarity[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2017,6(4).
APA	Deng, Xiaolong,Li, Lihui,&Tan, Yufang.(2017).Validation of Spatial Prediction Models for Landslide Susceptibility Mapping by Considering Structural Similarity.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,6(4).
MLA	Deng, Xiaolong,et al."Validation of Spatial Prediction Models for Landslide Susceptibility Mapping by Considering Structural Similarity".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 6.4(2017).