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Flood risk assessment using regional regression analysis

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  • Young-Oh Kim
  • Seung Seo
  • Ock-Jae Jang

Abstract

This study aimed to create a flood risk map for ungauged regions, which have limited flood damage data and other relevant data. The fact that there is a shortage of data that are critical for the establishment of a flood assessment and mitigation plan is not surprising even in developed countries like South Korea. To address this problem, the regional regression concept in statistical hydrology was introduced to the flood risk assessment field in this study, and it was framed with a series of two regression functions: flood damage and regional coefficients. As the second regression function utilizes the local socioeconomic variables, the resulting flood risk map can reflect the spatial characteristics well. The proposed methodology was applied to create flood risk maps for the three metropolitan areas in South Korea. The comparison of the proposed methodology with the existing methods revealed that only the proposed methodology can produce a statistically meaningful flood risk map based on a recent major flood in 2001. Copyright Springer Science+Business Media B.V. 2012

Suggested Citation

  • Young-Oh Kim & Seung Seo & Ock-Jae Jang, 2012. "Flood risk assessment using regional regression analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 63(2), pages 1203-1217, September.
    • Handle: RePEc:spr:nathaz:v:63:y:2012:i:2:p:1203-1217
    DOI: 10.1007/s11069-012-0221-6
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    Citations

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    Cited by:

    1. HaiBo Hu, 2016. "Rainstorm flash flood risk assessment using genetic programming: a case study of risk zoning in Beijing," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(1), pages 485-500, August.
    2. Hyun Il Choi, 2021. "Development of Flood Damage Regression Models by Rainfall Identification Reflecting Landscape Features in Gangwon Province, the Republic of Korea," Land, MDPI, vol. 10(2), pages 1-14, January.
    3. Seunghoo Jeong & D. K. Yoon, 2018. "Examining Vulnerability Factors to Natural Disasters with a Spatial Autoregressive Model: The Case of South Korea," Sustainability, MDPI, vol. 10(5), pages 1-13, May.
    4. Mohammad Mojtahedi & Sidney Newton & Jason Meding, 2017. "Predicting the resilience of transport infrastructure to a natural disaster using Cox’s proportional hazards regression model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 85(2), pages 1119-1133, January.
    5. Jui-Sheng Chou & Kuo-Hsin Yang & Min-Yuan Cheng & Wan-Ting Tu, 2013. "Identification and assessment of heavy rainfall–induced disaster potentials in Taipei City," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 66(2), pages 167-190, March.
    6. Deng-Hua Yan & Jing Feng & Chuan-Zhe Li & Tian-Ling Qin & Bai-Sha Weng & Xin Jin, 2012. "General framework and key issues concerning integrated strategies for coping with drought and flood in China in a changing environment," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 64(1), pages 577-592, October.

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