A GIS-Based Support Vector Machine Model for Flash Flood Vulnerability Assessment and Mapping in China
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. View Full-Text
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Xiong, J.; Li, J.; Cheng, W.; Wang, N.; Guo, L. A GIS-Based Support Vector Machine Model for Flash Flood Vulnerability Assessment and Mapping in China. ISPRS Int. J. Geo-Inf. 2019, 8, 297.
Xiong J, Li J, Cheng W, Wang N, Guo L. A GIS-Based Support Vector Machine Model for Flash Flood Vulnerability Assessment and Mapping in China. ISPRS International Journal of Geo-Information. 2019; 8(7):297.Chicago/Turabian Style
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 Int. J. Geo-Inf. 8, no. 7: 297.
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