Urban flood susceptibility analysis using a GIS-based multi-criteria analysis framework

Lin Lin, Zening Wu () and Qiuhua Liang
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Lin Lin: Zhengzhou University
Zening Wu: Zhengzhou University
Qiuhua Liang: Loughborough University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2019, vol. 97, issue 2, No 1, 455-475

Abstract: Abstract Pluvial flooding is a common type of natural hazard caused by rainfall events with high intensity and short duration, which may lead to substantial property damages, transportation interruptions, and casualties. Modern cities are susceptible to pluvial flooding due to dense population and advanced economic development. To facilitate the development of better flood control and risk mitigation strategies, this study presents a new quantitative flood susceptibility analysis framework to estimate the potential flood extents and scale. The framework is based on the multi-criteria decision-making methods within a platform of the geographic information system (GIS). A composite urban flood risk index (FRI) is derived from various flood conditioning factors. The FRI consists of flood vulnerability index, hazard factors, and resilience capacity indicators. The flood-susceptible map is generated using the GIS spatial analysis tools and the analytic hierarchy process method. Zhengzhou city, China, is selected as the case study area. The result map shows that the highly susceptible areas are mainly located in Jinshui District, accounting for 64% of the total area of the risk zone. To further validate this framework, a flood inventory map is produced by mapping 74 test locations identified through survey data in this area, followed by plotting a receiver operating characteristic (ROC) curve. The ROC shows an area under the curve of 74.27%, which validates the proposed framework. Compared with other methods, the proposed framework is particularly suitable for application in data-scarce cases.

Keywords: Susceptibility; MCDM; GIS; AHP (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (9)

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DOI: 10.1007/s11069-019-03615-2

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