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Assessment of Building Damage Risk by Natural Disasters in South Korea Using Decision Tree Analysis

Author

Listed:
  • KeumJi Kim

    (Department of Architecture, Pusan National University, Pusan 46241, Korea)

  • SeongHwan Yoon

    (Department of Architecture, Pusan National University, Pusan 46241, Korea)

Abstract

The purpose of this study is to identify the relationship between weather variables and buildings damaged in natural disasters. We used four datasets on building damage history and 33 weather datasets from 230 regions in South Korea in a decision tree analysis to evaluate the risk of building damage. We generated the decision tree model to determine the risk of rain, gale, and typhoon (excluding gale with less damage). Using the weight and limit values of the weather variables derived using the decision tree model, the risk of building damage was assessed for 230 regions in South Korea until 2100. The number of regions at risk of rain damage increased by more than 30% on average. Conversely, regions at risk of damage from snowfall decreased by more than 90%. The regions at risk of typhoons decreased by 57.5% on average, while those at high risk of the same increased by up to 62.5% under RCP 8.5. The results of this study are highly fluid since they are based on the uncertainty of future climate change. However, the study is meaningful because it suggests a new method for assessing disaster risk using weather indices.

Suggested Citation

  • KeumJi Kim & SeongHwan Yoon, 2018. "Assessment of Building Damage Risk by Natural Disasters in South Korea Using Decision Tree Analysis," Sustainability, MDPI, vol. 10(4), pages 1-22, April.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:4:p:1072-:d:139534
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    References listed on IDEAS

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    Cited by:

    1. Peng Ye & Xueying Zhang & Ge Shi & Shuhui Chen & Zhiwen Huang & Wei Tang, 2020. "TKRM: A Formal Knowledge Representation Method for Typhoon Events," Sustainability, MDPI, vol. 12(5), pages 1-19, March.
    2. Jihye Han & Soyoung Park & Seongheon Kim & Sanghun Son & Seonghyeok Lee & Jinsoo Kim, 2019. "Performance of Logistic Regression and Support Vector Machines for Seismic Vulnerability Assessment and Mapping: A Case Study of the 12 September 2016 ML5.8 Gyeongju Earthquake, South Korea," Sustainability, MDPI, vol. 11(24), pages 1-19, December.

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