A GIS-Based Support Vector Machine Model for Flash Flood Vulnerability Assessment and Mapping in China

被引:72
作者
Xiong, Junnan [1 ,2 ]
Li, Jin [1 ]
Cheng, Weiming [2 ,3 ,4 ]
Wang, Nan [2 ]
Guo, Liang [5 ]
机构
[1] 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
关键词
GIS; flash flood vulnerability assessment; exposure; disaster reduction capability; SVM; China; ANALYTICAL HIERARCHY PROCESS; LANDSLIDE SUSCEPTIBILITY; RISK-ASSESSMENT; NATURAL DISASTERS; SOCIAL VULNERABILITY; EXTREME RAINFALL; HAZARD; RESILIENCE; REGION; AHP;
D O I
10.3390/ijgi8070297
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Flash 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.
引用
收藏
页数:23
相关论文
共 69 条
[51]   Analysis of flash flood parameters and human impacts in the US from 2006 to 2012 [J].
Spitalar, Marusa ;
Gourley, Jonathan J. ;
Lutoff, Celine ;
Kirstetter, Pierre-Emmanuel ;
Brilly, Mitja ;
Carr, Nicholas .
JOURNAL OF HYDROLOGY, 2014, 519 :863-870
[52]   Accelerated Urban Expansion in Lhasa City and the Implications for Sustainable Development in a Plateau City [J].
Tang, Wei ;
Zhou, Tiancai ;
Sun, Jian ;
Li, Yurui ;
Li, Weipeng .
SUSTAINABILITY, 2017, 9 (09)
[53]   Mountain torrents: Quantifying vulnerability and assessing uncertainties [J].
Totschnig, Reinhold ;
Fuchs, Sven .
ENGINEERING GEOLOGY, 2013, 155 :31-44
[54]   A conceptual framework for quantitative estimation of physical vulnerability to landslides [J].
Uzielli, Marco ;
Nadim, Farrokh ;
Lacasse, Suzanne ;
Kaynia, Amir M. .
ENGINEERING GEOLOGY, 2008, 102 (3-4) :251-256
[55]   Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview [J].
van Westen, Cees J. ;
Castellanos, Enrique ;
Kuriakose, Sekhar L. .
ENGINEERING GEOLOGY, 2008, 102 (3-4) :112-131
[56]  
Velasco E., 2010, EGU GEN ASSEM, V12, P10275
[57]   Landslide risk assessment in a densely populated hilly area [J].
Vranken, Liesbet ;
Vantilt, Goele ;
Van den Eeckhaut, Miet ;
Vandekerckhove, Liesbeth ;
Poesen, Jean .
LANDSLIDES, 2015, 12 (04) :787-798
[58]   Economic valuation of landslide damage in hilly regions: A case study from Flanders, Belgium [J].
Vranken, Liesbet ;
Van Turnhout, Pieter ;
Van den Eeckhaut, Miet ;
Vandekerckhove, Liesbeth ;
Poesen, Jean .
SCIENCE OF THE TOTAL ENVIRONMENT, 2013, 447 :323-336
[59]   The assessment of vulnerability to natural disasters in China by using the DEA method [J].
Wei, YM ;
Fan, Y ;
Lu, C ;
Tsai, HT .
ENVIRONMENTAL IMPACT ASSESSMENT REVIEW, 2004, 24 (04) :427-439
[60]   A New Fuzzy Comprehensive Evaluation Model Based on the Support Vector Machine [J].
Xian, Si-dong .
FUZZY INFORMATION AND ENGINEERING, 2010, 2 (01) :75-86