IGSNRR OpenIR
Local-scale landslide susceptibility mapping using the B-GeoSVC model
Yang, Yang1; Yang, Jintao1,2; Xu, Chengdong2; Xu, Chong3; Song, Chao1,2
2019-07-01
Source PublicationLANDSLIDES
ISSN1612-510X
Volume16Issue:7Pages:1301-1312
Corresponding AuthorSong, Chao(chaosong.gis@gmail.com)
AbstractLocal-scale landslide susceptibility mapping (LSM) provides detailed information for decision making and planning. Most published landslide susceptibility maps lack reliable information at the local scale due to the spatial heterogeneity being ignored. To enrich the local-scale information of LSM, multiple information fusion methods for the local spatial heterogeneity and regional trends of control factors are needed. However, no information fusion method has been proposed for LSM yet. In this paper, we developed a new integrated statistical method, named B-GeoSVC, under the hierarchical Bayesian framework for LSM. Specifically, this model applied the GeoDetector method to fit the regional trends of control factors and employed spatially varying coefficients (SVC) model to fit the local spatial heterogeneity of each control factor. Then, the regional trends and local spatial heterogeneity information were fused within the hierarchical Bayesian framework. The B-GeoSVC model was verified using data from the Duwen basin of China, which was in the central region affected by the M-S 8.0 Wenchuan earthquake that occurred on May 12, 2008. Under a cross-validation experiment, the prediction accuracy rate of the B-GeoSVC model was 86.09%, and the area under the curve was 0.93, which suggested that the B-GeoSVC model was able to achieve relatively accurate local-scale LSM and provide richer local information than traditional regional scale LSM. More importantly, not only the B-GeoSVC model could be employed as a general solution to fuse both regional and local-scale information for landslide mapping, but also offer new insights into the broader earth science and spatial statistics.
KeywordLandslide susceptibility mapping Spatial heterogeneity Regional and local information fusion GeoDetector SVC Hierarchical Bayesian method
DOI10.1007/s10346-019-01174-y
WOS KeywordLOGISTIC-REGRESSION ; RISK ASSESSMENT ; HAZARD ; CLASSIFIER ; DECISION ; SICHUAN
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41701448] ; State Key Laboratory of Resources and Environmental Information System[201811] ; Young Scholars Development Fund of Southwest Petroleum University[201699010064] ; Open Fund of the State Key Laboratory of Geoscience Spatial Information Technology, Ministry of Land and Resource[KLGSIT2016-03] ; Technology Project of the Sichuan Bureau of Surveying, Mapping and Geoinformation[J2017ZC05] ; Science and Technology Strategy School Cooperation Projects of the Nanchong City Science and Technology Bureau[NC17SY4016] ; Science and Technology Strategy School Cooperation Projects of the Nanchong City Science and Technology Bureau[18SXHZ0025]
Funding OrganizationNational Natural Science Foundation of China ; State Key Laboratory of Resources and Environmental Information System ; Young Scholars Development Fund of Southwest Petroleum University ; Open Fund of the State Key Laboratory of Geoscience Spatial Information Technology, Ministry of Land and Resource ; Technology Project of the Sichuan Bureau of Surveying, Mapping and Geoinformation ; Science and Technology Strategy School Cooperation Projects of the Nanchong City Science and Technology Bureau
WOS Research AreaEngineering ; Geology
WOS SubjectEngineering, Geological ; Geosciences, Multidisciplinary
WOS IDWOS:000471663200004
PublisherSPRINGER HEIDELBERG
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/58916
Collection中国科学院地理科学与资源研究所
Corresponding AuthorSong, Chao
Affiliation1.Southwest Petr Univ, Sch Geosci & Technol, Chengdu 610500, Sichuan, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
3.China Earthquake Adm, Inst Geol, Beijing 100029, Peoples R China
Recommended Citation
GB/T 7714
Yang, Yang,Yang, Jintao,Xu, Chengdong,et al. Local-scale landslide susceptibility mapping using the B-GeoSVC model[J]. LANDSLIDES,2019,16(7):1301-1312.
APA Yang, Yang,Yang, Jintao,Xu, Chengdong,Xu, Chong,&Song, Chao.(2019).Local-scale landslide susceptibility mapping using the B-GeoSVC model.LANDSLIDES,16(7),1301-1312.
MLA Yang, Yang,et al."Local-scale landslide susceptibility mapping using the B-GeoSVC model".LANDSLIDES 16.7(2019):1301-1312.
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