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A Bayesian Network-Based Integrated for Flood Risk Assessment (InFRA)

Author

Listed:
  • Hongjun Joo

    (Department of Civil Engineering, Inha University, Incheon 22212, Korea)

  • Changhyun Choi

    (Department of Civil Engineering, Inha University, Incheon 22212, Korea)

  • Jungwook Kim

    (Department of Civil Engineering, Inha University, Incheon 22212, Korea)

  • Deokhwan Kim

    (Department of Land, Water and Environment Research, Korea Institute of Civil Engineering and Building Technology, Ilsan 10223, Korea)

  • Soojun Kim

    (Department of Civil Engineering, Inha University, Incheon 22212, Korea)

  • Hung Soo Kim

    (Department of Civil Engineering, Inha University, Incheon 22212, Korea)

Abstract

Floods are natural disasters that should be considered a top priority in disaster management, and various methods have been developed to evaluate the risks. However, each method has different results and may confuse decision-makers in disaster management. In this study, a flood risk assessment method is proposed to integrate various methods to overcome these problems. Using factor analysis and principal component analysis (PCA), the leading indicators that affect flood damage were selected and weighted using three methods: the analytic hierarchy process (AHP), constant sum scale (CSS), and entropy. However, each method has flaws due to inconsistent weights. Therefore, a Bayesian network was used to present the integrated weights that reflect the characteristics of each method. Moreover, a relationship is proposed between the elements and the indicators based on the weights called the Integrated Index for Flood Risk Assessment (InFRA). InFRA and other assessment methods were compared by receiver operating characteristics (ROC)-area under curve (AUC) analysis. As a result, InFRA showed better applicability since InFRA was 0.67 and other methods were less than 0.5.

Suggested Citation

  • Hongjun Joo & Changhyun Choi & Jungwook Kim & Deokhwan Kim & Soojun Kim & Hung Soo Kim, 2019. "A Bayesian Network-Based Integrated for Flood Risk Assessment (InFRA)," Sustainability, MDPI, vol. 11(13), pages 1-15, July.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:13:p:3733-:d:246683
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    References listed on IDEAS

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    Cited by:

    1. Bikram Manandhar & Shenghui Cui & Lihong Wang & Sabita Shrestha, 2023. "Urban Flood Hazard Assessment and Management Practices in South Asia: A Review," Land, MDPI, vol. 12(3), pages 1-29, March.
    2. Shanqing Huang & Huimin Wang & Yejun Xu & Jingwen She & Jing Huang, 2021. "Key Disaster-Causing Factors Chains on Urban Flood Risk Based on Bayesian Network," Land, MDPI, vol. 10(2), pages 1-21, February.
    3. Hariklia D. Skilodimou & George D. Bathrellos, 2021. "Natural and Technological Hazards in Urban Areas: Assessment, Planning and Solutions," Sustainability, MDPI, vol. 13(15), pages 1-5, July.
    4. Wen-Cheng Liu & Tien-Hsiang Hsieh & Hong-Ming Liu, 2021. "Flood Risk Assessment in Urban Areas of Southern Taiwan," Sustainability, MDPI, vol. 13(6), pages 1-22, March.

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