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Identifying Risk Indicators for Natural Hazard-Related Power Outages as a Component of Risk Assessment: An Analysis Using Power Outage Data from Hurricane Irma

Author

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  • Sang-Guk Yum

    (Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, NY 10027, USA)

  • Kiyoung Son

    (School of Architectural Engineering, Ulsan University, Ulsan 44610, Korea)

  • Seunghyun Son

    (Department of Architectural Engineering, Kyung Hee University, Suwon 17104, Korea)

  • Ji-Myong Kim

    (Department of Architectural Engineering, Mokpo National University, Mokpo 58554, Korea)

Abstract

Extensive use has been made of lifecycle-cost assessment to enhance the cost-effectiveness and resilience of facilities management. However, if such assessments are to be truly effective, supplemental information will be needed on the major costs to be expected over buildings’ entire lives. Electricity generation and distribution systems, for example, are absolutely indispensable to industry and human society, not least in the operation of buildings and other infrastructure as networks. The widespread disruption that ensues when such power systems are damaged often carries considerable repair costs. Natural disasters likewise can cause extensive societal, economic, and environmental damage. Such damage is often associated with lengthy power outages that, as well as being directly harmful, can hinder emergency response and recovery. Accordingly, the present study investigated the correlations of natural hazard indicators such as wind speed and rainfall, along with environmental data regarding the power failure in Florida caused by Hurricane Irma in 2017 utilizing multiple regression analysis. The environmental data in question, selected on the basis of a thorough literature review, was tree density. Our analysis indicated that the independent variables, maximum wind speed, total rainfall, and tree density, were all significantly correlated with the dependent variable, power failure. Among these, rainfall was the least significant. Despite there being only three independent variables in the model, its adjusted coefficient of determination (0.512) indicated its effectiveness as a predictor of the power outages caused by Hurricane Irma. As such, our results can serve the construction industry’s establishment of advanced safety guidelines and structural designs power transmission systems in regions at risk of hurricanes and typhoons. Additionally, insurance companies’ loss-assessment modeling for power-system facilities would benefit from incorporating the three identified risk indicators. Finally, our findings can serve as a useful reference to policymakers tasked with mitigating power outages’ effects on infrastructure in hurricane-prone areas. It is hoped that this work will be extended, facilitating infrastructure restoration planning and making societies and economies more sustainable.

Suggested Citation

  • Sang-Guk Yum & Kiyoung Son & Seunghyun Son & Ji-Myong Kim, 2020. "Identifying Risk Indicators for Natural Hazard-Related Power Outages as a Component of Risk Assessment: An Analysis Using Power Outage Data from Hurricane Irma," Sustainability, MDPI, vol. 12(18), pages 1-15, September.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:18:p:7702-:d:415169
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    References listed on IDEAS

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    1. Seung‐Ryong Han & Seth D. Guikema & Steven M. Quiring, 2009. "Improving the Predictive Accuracy of Hurricane Power Outage Forecasts Using Generalized Additive Models," Risk Analysis, John Wiley & Sons, vol. 29(10), pages 1443-1453, October.
    2. Menoka Bal & David Bryde & Damian Fearon & Edward Ochieng, 2013. "Stakeholder Engagement: Achieving Sustainability in the Construction Sector," Sustainability, MDPI, vol. 5(2), pages 1-16, February.
    3. Sang-Guk Yum & Ji-Myong Kim & Kiyoung Son, 2020. "Natural Hazard Influence Model of Maintenance and Repair Cost for Sustainable Accommodation Facilities," Sustainability, MDPI, vol. 12(12), pages 1-11, June.
    4. Jonathan Remo & Nicholas Pinter, 2012. "Hazus-MH earthquake modeling in the central USA," 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 1055-1081, September.
    5. Ji-Myong Kim & Seunghyun Son & Sungho Lee & Kiyoung Son, 2020. "Cost of Climate Change: Risk of Building Loss from Typhoon in South Korea," Sustainability, MDPI, vol. 12(17), pages 1-11, August.
    6. Alena Rein & Ross Corotis, 2013. "An overview approach to seismic awareness for a “quiescent” region," 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. 67(2), pages 335-363, June.
    7. Jufri, Fauzan Hanif & Widiputra, Victor & Jung, Jaesung, 2019. "State-of-the-art review on power grid resilience to extreme weather events: Definitions, frameworks, quantitative assessment methodologies, and enhancement strategies," Applied Energy, Elsevier, vol. 239(C), pages 1049-1065.
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    Cited by:

    1. Sunkuk Kim, 2021. "Technology and Management for Sustainable Buildings and Infrastructures," Sustainability, MDPI, vol. 13(16), pages 1-3, August.
    2. Yongjun Chen & Xiaojian Li & Jin Wang & Mei Liu & Chaoxun Cai & Yuefeng Shi, 2023. "Research on the Application of Fuzzy Bayesian Network in Risk Assessment of Catenary Construction," Mathematics, MDPI, vol. 11(7), pages 1-19, April.
    3. Hasan M. Salman & Jagadeesh Pasupuleti & Ahmad H. Sabry, 2023. "Review on Causes of Power Outages and Their Occurrence: Mitigation Strategies," Sustainability, MDPI, vol. 15(20), pages 1-34, October.

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