IMHE OpenIR  > 山地灾害与地表过程重点实验室
Landslide integrated characteristics and susceptibility assessment in Rongxian county of Guangxi, China
Liao Li-ping1,2,3; Zhu Ying-yan4; Zhao Yan-lin1; Wen Hai-tao5; Yang Yun-chuan1,2,3; Chen Li-hua1; Ma Shao-kun1; Xu Ying-zi1
Corresponding AuthorWen Hai-tao(wavewen500@163.com)
2019-03-01
Source PublicationJOURNAL OF MOUNTAIN SCIENCE
ISSN1672-6316
Volume16Issue:3Pages:657-676
AbstractLandslides distribute extensively in Rongxian county, the southeast of Guangxi province, China and pose great threats to this county. At present, hazard management strategy is facing an unprecedented challenge due to lack of a landslide susceptibility map. Therefore, the purpose of this paper was to construct a landslide susceptibility map by adopting three widely used models based on an integrated understanding of landslide's characteristics. These models include a semi-quantitative method (SQM), information value model (IVM) and logistical regression model (LRM).The primary results show that (1) the county is classified into four susceptive regions, named as very low, low, moderate and high, which covered an area of 13.43%, 32.40%, 31.19% and 22.99% in SQM, 0.86%, 26.82%, 44.11%, and 28.21% in IVM, 9.88%, 17.73%, 46.36% and 26.03% in LRM; (2) landslides are likely to occur within the areas characterized by following obvious aspects: high intensity of human activities, slope angles of 25 degrees similar to 35 degrees, the thickness of weathered soil is larger than 15 m; the lithology is granite, shale and mud rock; (3) the area under the curve of SQM, IVM and LRM is 0.7151, 0.7688 and 0.7362 respectively, and the corresponding success rate is 71.51%, 76.88% and 73.62%. It is concluded that these three models are acceptable because they have an effective capability of susceptibility assessment and can achieve an expected accuracy. In addition, the susceptibility outcome obtained from IVM provides a slightly higher quality than that from SQM, LRM.
KeywordLandslide characteristic Susceptibility zonation Prevention regionalization Rongxian county
DOI10.1007/s11629-017-4804-2
Indexed BySCI
WOS KeywordANALYTIC HIERARCHY PROCESS ; WEIGHTED LINEAR COMBINATION ; LOGISTIC-REGRESSION MODELS ; ARTIFICIAL NEURAL-NETWORK ; INFORMATION VALUE METHOD ; HAZARD RISK-ASSESSMENT ; FREQUENCY RATIO ; STATISTICAL INDEX ; CERTAINTY FACTOR ; DECISION TREE
Language英语
Funding ProjectNational Natural Science Foundation of China[51609041] ; Natural Scientific Project of Guangxi Zhuang Autonomous Region[2018GXNSFAA138187] ; Project of the Education Department of Guangxi Zhuang Autonomous Region[2018KY0027] ; Project of Department of Land and Resources of Guangxi Zhuang Autonomous Region[GXZC2018-G3-19302-JGYZ] ; Project of Xi'an Geological survey center of China Geological survey[DD20189270]
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEnvironmental Sciences
WOS IDWOS:000460754100013
Funding OrganizationNational Natural Science Foundation of China ; Natural Scientific Project of Guangxi Zhuang Autonomous Region ; Project of the Education Department of Guangxi Zhuang Autonomous Region ; Project of Department of Land and Resources of Guangxi Zhuang Autonomous Region ; Project of Xi'an Geological survey center of China Geological survey
PublisherSCIENCE PRESS
Citation statistics
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/25310
Collection山地灾害与地表过程重点实验室
Corresponding AuthorWen Hai-tao
Affiliation1.Guangxi Univ, Coll Civil Engn & Architecture, Nanning 530004, Peoples R China
2.Guangxi Univ, Key Lab Disaster Prevent & Struct Safety, Minist Educ, Nanning 530004, Peoples R China
3.Guangxi Univ, Guangxi Key Lab Disaster Prevent & Engn Safety, Nanning 530004, Peoples R China
4.Chinese Acad Sci, Key Lab Mt Hazards & Surface Proc, Inst Mt Hazards & Environm, Chengdu 610041, Sichuan, Peoples R China
5.Guangxi Zhuang Autonomous Reg Geol Environm Monit, Guilin 541000, Peoples R China
Recommended Citation
GB/T 7714
Liao Li-ping,Zhu Ying-yan,Zhao Yan-lin,et al. Landslide integrated characteristics and susceptibility assessment in Rongxian county of Guangxi, China[J]. JOURNAL OF MOUNTAIN SCIENCE,2019,16(3):657-676.
APA Liao Li-ping.,Zhu Ying-yan.,Zhao Yan-lin.,Wen Hai-tao.,Yang Yun-chuan.,...&Xu Ying-zi.(2019).Landslide integrated characteristics and susceptibility assessment in Rongxian county of Guangxi, China.JOURNAL OF MOUNTAIN SCIENCE,16(3),657-676.
MLA Liao Li-ping,et al."Landslide integrated characteristics and susceptibility assessment in Rongxian county of Guangxi, China".JOURNAL OF MOUNTAIN SCIENCE 16.3(2019):657-676.
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