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Assessment of flood susceptibility mapping using support vector machine, logistic regression and their ensemble techniques in the Belt and Road region

文献类型：期刊论文

作者Liu, Jun2; Wang, Jiyan2; Xiong, Junnan2,3; Cheng, Weiming1,3; Li, Yi4; Cao, Yifan2; He, Yufeng2; Duan, Yu2; He, Wen2; Yang, Gang2
刊名GEOCARTO INTERNATIONAL
出版日期2022-01-10
页码30
ISSN号1010-6049
关键词Food susceptibility ensemble modeling support vector machine logistic regression the Belt and Road region
DOI10.1080/10106049.2022.2025918
通讯作者Xiong, Junnan(xiongjn@swpu.edu.cn)
英文摘要Floods have occurred frequently all over the world. During 2000-2020, nearly half (44.9%) of global floods occurred in the Belt and Road region because of its complex geology, topography, and climate. Therefore, providing an insight into the spatial distribution characteristics of flood susceptibility in this region is essential. Here, a database was established with 11 flood conditioning factors, 1500 flooded points, and 1500 non-flooded points selected by an improved method. Subsequently, a rare combination of logistic regression and support vector machine, integrated by heterogeneous framework, was applied to generate an ensemble flood susceptibility map. Based on it, the concept of ecological vulnerability synthesis index in the ecological field was introduced into this study, and the flood susceptibility comprehensive index (FSCI) was proposed to quantify the degree of flood susceptibility of each country and sub-region. At the results, the ensemble model has an excellent accuracy, with the highest AUC value of 0.9342. The highest and high flood susceptibility zones are mainly located in the southeastern part of Eastern Asia, most of Southeast Asia and South Asia, account for 12.22% and 9.57% of the total study area, respectively. From the regional perspective, it can be found that Southeast Asia had the highest flood susceptibility with the highest FSCI of 4.69, while East Asia and Central and Eastern Europe showed the most significant spatial distribution characteristics. From the national perspective, of the 66 countries in this region, 20 of the countries have the highest flood susceptibility level (FSCIn > 0.8), which face the greatest threat of flooding. These results are able to facilitate reasonable flood mitigation measures develop at the most critical locations in the Belt and Road region and lays a theoretical basis for quantifying flood susceptibility at national or regional scale.
WOS关键词ANALYTIC HIERARCHY PROCESS ; INFORMATION VALUE METHOD ; WEIGHTS-OF-EVIDENCE ; LANDSLIDE SUSCEPTIBILITY ; RISK-ASSESSMENT ; FREQUENCY RATIO ; STATISTICAL-MODELS ; SPATIAL PREDICTION ; ARTIFICIAL-INTELLIGENCE ; CONDITIONING FACTORS
资助项目Key R&D project of Sichuan Science and Technology Department[2021YFQ0042] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA20030302] ; National Flash Flood Investigation and Evaluation Project[SHZH-IWHR-57] ; National Key R&D Program of China[2020YFD1100701] ; Science and Technology Project of Xizang Autonomous Region[XZ201901-GA-07] ; Science and Technology Bureau of Altay Region in Yili Kazak Autonomous Prefecture
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者TAYLOR & FRANCIS LTD
WOS记录号WOS:000742474300001
资助机构Key R&D project of Sichuan Science and Technology Department ; Strategic Priority Research Program of the Chinese Academy of Sciences ; National Flash Flood Investigation and Evaluation Project ; National Key R&D Program of China ; Science and Technology Project of Xizang Autonomous Region ; Science and Technology Bureau of Altay Region in Yili Kazak Autonomous Prefecture
源URL[http://ir.igsnrr.ac.cn/handle/311030/169611]  
专题中国科学院地理科学与资源研究所
通讯作者Xiong, Junnan
作者单位1.Univ Chinese Acad Sci, Beijing, Peoples R China
2.Southwest Petr Univ, Sch Civil Engn & Geomat, Chengdu, Peoples R China
3.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
4.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Jun,Wang, Jiyan,Xiong, Junnan,et al. Assessment of flood susceptibility mapping using support vector machine, logistic regression and their ensemble techniques in the Belt and Road region[J]. GEOCARTO INTERNATIONAL,2022:30.
APA Liu, Jun.,Wang, Jiyan.,Xiong, Junnan.,Cheng, Weiming.,Li, Yi.,...&Yang, Gang.(2022).Assessment of flood susceptibility mapping using support vector machine, logistic regression and their ensemble techniques in the Belt and Road region.GEOCARTO INTERNATIONAL,30.
MLA Liu, Jun,et al."Assessment of flood susceptibility mapping using support vector machine, logistic regression and their ensemble techniques in the Belt and Road region".GEOCARTO INTERNATIONAL (2022):30.

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来源：地理科学与资源研究所

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