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A GIS-Based Support Vector Machine Model for Flash Flood Vulnerability Assessment and Mapping in China
Xiong, Junnan1,2; Li, Jin1; Cheng, Weiming2,3,4; Wang, Nan2; Guo, Liang5
2019-07-01
Source PublicationISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
ISSN2220-9964
Volume8Issue:7Pages:23
Corresponding AuthorCheng, Weiming(chengwm@lreis.ac.cn)
AbstractFlash floods are one of the natural disasters that threaten the lives of many people all over the world every year. Flash floods are significantly affected by the intensification of extreme climate events and interactions with exposed and vulnerable socio-economic systems impede regional development processes. Hence, it is important to estimate the loss due to flash floods before the disaster occurs. However, there are no comprehensive vulnerability assessment results for flash floods in China. Fortunately, the National Mountain Flood Disaster Investigation Project provided a foundation to develop this proposed assessment. In this study, an index system was established from the exposure and disaster reduction capability categories, and is based on analytic hierarchy process (AHP) methods. We evaluated flash flood vulnerability by adopting the support vector machine (SVM) model. Our results showed 439 counties with high and extremely high vulnerability (accounting for 10.5% of the land area and corresponding to approximately 100 million hectares (ha)), 571 counties with moderate vulnerability (accounting for 19.18% of the land area and corresponding to approximately 180 million ha), and 1128 counties with low and extremely low vulnerability (accounting for 39.43% of the land area and corresponding to approximately 370 million ha). The highly-vulnerable counties were mainly concentrated in the south and southeast regions of China, moderately-vulnerable counties were primarily concentrated in the central, northern, and southwestern regions of China, and low-vulnerability counties chiefly occurred in the northwest regions of China. Additionally, the results of the spatial autocorrelation suggested that the "High-High" values of spatial agglomeration areas mainly occurred in the Zhejiang, Fujian, Jiangxi, Hunan, Guangxi, Chongqing, and Beijing areas. On the basis of these results, our study can be used as a proposal for population and building distribution readjustments, and the management of flash floods in China.
KeywordGIS flash flood vulnerability assessment exposure disaster reduction capability SVM China
DOI10.3390/ijgi8070297
WOS KeywordANALYTICAL HIERARCHY PROCESS ; LANDSLIDE SUSCEPTIBILITY ; RISK-ASSESSMENT ; NATURAL DISASTERS ; SOCIAL VULNERABILITY ; EXTREME RAINFALL ; HAZARD ; RESILIENCE ; REGION ; AHP
Indexed BySCI
Language英语
Funding ProjectChina Academy of Sciences Strategic Leading Science and Technology Project[XDA20030302] ; National Mountain Flood Disaster Survey and Evaluation Project of Chinese Academy of Water Sciences[SHZH-IWHR-57] ; China Geological Survey Project[DD20190637] ; Science and Technology Project of Xizang Autonomous Region[XZ201901-GA-07] ; Open Topic of Digital Fujian Institute of Large Data for Natural Disaster Monitoring[NDMBD2018003] ; Scientific and Technological Innovation Team Project of Southwest Petroleum University[2017CXTD09]
Funding OrganizationChina Academy of Sciences Strategic Leading Science and Technology Project ; National Mountain Flood Disaster Survey and Evaluation Project of Chinese Academy of Water Sciences ; China Geological Survey Project ; Science and Technology Project of Xizang Autonomous Region ; Open Topic of Digital Fujian Institute of Large Data for Natural Disaster Monitoring ; Scientific and Technological Innovation Team Project of Southwest Petroleum University
WOS Research AreaPhysical Geography ; Remote Sensing
WOS SubjectGeography, Physical ; Remote Sensing
WOS IDWOS:000478616400008
PublisherMDPI
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/58285
Collection中国科学院地理科学与资源研究所
Corresponding AuthorCheng, Weiming
Affiliation1.Southwest Petr Univ, Sch Civil Engn & Architecture, 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.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
5.China Inst Water Resources & Hydropower Res, Beijing 100038, Peoples R China
Recommended Citation
GB/T 7714
Xiong, Junnan,Li, Jin,Cheng, Weiming,et al. A GIS-Based Support Vector Machine Model for Flash Flood Vulnerability Assessment and Mapping in China[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2019,8(7):23.
APA Xiong, Junnan,Li, Jin,Cheng, Weiming,Wang, Nan,&Guo, Liang.(2019).A GIS-Based Support Vector Machine Model for Flash Flood Vulnerability Assessment and Mapping in China.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,8(7),23.
MLA Xiong, Junnan,et al."A GIS-Based Support Vector Machine Model for Flash Flood Vulnerability Assessment and Mapping in China".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 8.7(2019):23.
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