IDEAS home Printed from https://ideas.repec.org/a/wly/riskan/v39y2019i9p1930-1948.html
   My bibliography  Save this article

Quantifying Community Resilience Using Hierarchical Bayesian Kernel Methods: A Case Study on Recovery from Power Outages

Author

Listed:
  • Jin‐Zhu Yu
  • Hiba Baroud

Abstract

The ability to accurately measure recovery rate of infrastructure systems and communities impacted by disasters is vital to ensure effective response and resource allocation before, during, and after a disruption. However, a challenge in quantifying such measures resides in the lack of data as community recovery information is seldom recorded. To provide accurate community recovery measures, a hierarchical Bayesian kernel model (HBKM) is developed to predict the recovery rate of communities experiencing power outages during storms. The performance of the proposed method is evaluated using cross‐validation and compared with two models, the hierarchical Bayesian regression model and the Poisson generalized linear model. A case study focusing on the recovery of communities in Shelby County, Tennessee after severe storms between 2007 and 2017 is presented to illustrate the proposed approach. The predictive accuracy of the models is evaluated using the log‐likelihood and root mean squared error. The HBKM yields on average the highest out‐of‐sample predictive accuracy. This approach can help assess the recoverability of a community when data are scarce and inform decision making in the aftermath of a disaster. An illustrative example is presented demonstrating how accurate measures of community resilience can help reduce the cost of infrastructure restoration.

Suggested Citation

  • Jin‐Zhu Yu & Hiba Baroud, 2019. "Quantifying Community Resilience Using Hierarchical Bayesian Kernel Methods: A Case Study on Recovery from Power Outages," Risk Analysis, John Wiley & Sons, vol. 39(9), pages 1930-1948, September.
    • Handle: RePEc:wly:riskan:v:39:y:2019:i:9:p:1930-1948
    DOI: 10.1111/risa.13343
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/risa.13343
    Download Restriction: no
    File URL: https://libkey.io/10.1111/risa.13343?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Steven Quiring & Laiyin Zhu & Seth Guikema, 2011. "Importance of soil and elevation characteristics for modeling hurricane-induced power outages," 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. 58(1), pages 365-390, July.
    2. Yan, Zhenyu & Haimes, Yacov Y., 2010. "Cross-classified hierarchical Bayesian models for risk-based analysis of complex systems under sparse data," Reliability Engineering and System Safety, Elsevier, vol. 95(7), pages 764-776.
    3. Migon, Helio S. & Moura, Fernando A.S., 2005. "Hierarchical Bayesian collective risk model: an application to health insurance," Insurance: Mathematics and Economics, Elsevier, vol. 36(2), pages 119-135, April.
    4. Baroud, Hiba & Barker, Kash, 2018. "A Bayesian kernel approach to modeling resilience-based network component importance," Reliability Engineering and System Safety, Elsevier, vol. 170(C), pages 10-19.
    5. Leslie Gillespie‐Marthaler & Katherine Nelson & Hiba Baroud & Mark Abkowitz, 2019. "Selecting Indicators for Assessing Community Sustainable Resilience," Risk Analysis, John Wiley & Sons, vol. 39(11), pages 2479-2498, November.
    6. Faes, Christel & Geys, Helena & Aerts, Marc & Molenberghs, Geert, 2006. "A hierarchical modeling approach for risk assessment in developmental toxicity studies," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1848-1861, December.
    7. Andrade, A.R. & Teixeira, P.F., 2015. "Statistical modelling of railway track geometry degradation using Hierarchical Bayesian models," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 169-183.
    8. Yu Nie & Xing Wu & Tito Homem-de-Mello, 2012. "Optimal Path Problems with Second-Order Stochastic Dominance Constraints," Networks and Spatial Economics, Springer, vol. 12(4), pages 561-587, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Michael Greenberg & Anthony Cox & Vicki Bier & Jim Lambert & Karen Lowrie & Warner North & Michael Siegrist & Felicia Wu, 2020. "Risk Analysis: Celebrating the Accomplishments and Embracing Ongoing Challenges," Risk Analysis, John Wiley & Sons, vol. 40(S1), pages 2113-2127, November.
    2. S. E. Galaitsi & Jeffrey M. Keisler & Benjamin D. Trump & Igor Linkov, 2021. "The Need to Reconcile Concepts that Characterize Systems Facing Threats," Risk Analysis, John Wiley & Sons, vol. 41(1), pages 3-15, January.
    3. Yu, Jin-Zhu & Whitman, Mackenzie & Kermanshah, Amirhassan & Baroud, Hiba, 2021. "A hierarchical Bayesian approach for assessing infrastructure networks serviceability under uncertainty: A case study of water distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yu, Jin-Zhu & Whitman, Mackenzie & Kermanshah, Amirhassan & Baroud, Hiba, 2021. "A hierarchical Bayesian approach for assessing infrastructure networks serviceability under uncertainty: A case study of water distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Malavasi, Matteo & Ortobelli Lozza, Sergio & Trück, Stefan, 2021. "Second order of stochastic dominance efficiency vs mean variance efficiency," European Journal of Operational Research, Elsevier, vol. 290(3), pages 1192-1206.
    3. Yang, Lixing & Zhou, Xuesong, 2017. "Optimizing on-time arrival probability and percentile travel time for elementary path finding in time-dependent transportation networks: Linear mixed integer programming reformulations," Transportation Research Part B: Methodological, Elsevier, vol. 96(C), pages 68-91.
    4. Dimitris N. Trakas & Mathaios Panteli & Nikos D. Hatziargyriou & Pierluigi Mancarella, 2019. "Spatial Risk Analysis of Power Systems Resilience During Extreme Events," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 195-211, January.
    5. Gina L. Tonn & Seth D. Guikema & Celso M. Ferreira & Steven M. Quiring, 2016. "Hurricane Isaac: A Longitudinal Analysis of Storm Characteristics and Power Outage Risk," Risk Analysis, John Wiley & Sons, vol. 36(10), pages 1936-1947, October.
    6. Hughes, William & Zhang, Wei & Cerrai, Diego & Bagtzoglou, Amvrossios & Wanik, David & Anagnostou, Emmanouil, 2022. "A Hybrid Physics-Based and Data-Driven Model for Power Distribution System Infrastructure Hardening and Outage Simulation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    7. Leonardo Leoni & Farshad BahooToroody & Saeed Khalaj & Filippo De Carlo & Ahmad BahooToroody & Mohammad Mahdi Abaei, 2021. "Bayesian Estimation for Reliability Engineering: Addressing the Influence of Prior Choice," IJERPH, MDPI, vol. 18(7), pages 1-16, March.
    8. Mehmet Baran Ulak & Ayberk Kocatepe & Lalitha Madhavi Konila Sriram & Eren Erman Ozguven & Reza Arghandeh, 2018. "Assessment of the hurricane-induced power outages from a demographic, socioeconomic, and transportation perspective," 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. 92(3), pages 1489-1508, July.
    9. Salazar García, Juan Fernando & Guzmán Aguilar, Diana Sirley & Hoyos Nieto, Daniel Arturo, 2023. "Modelación de una prima de seguros mediante la aplicación de métodos actuariales, teoría de fallas y Black-Scholes en la salud en Colombia [Modelling of an insurance premium through the application," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 35(1), pages 330-359, June.
    10. Shengli, Liu & Yongtu, Liang, 2019. "Exploring the temporal structure of time series data for hazardous liquid pipeline incidents based on complex network theory," International Journal of Critical Infrastructure Protection, Elsevier, vol. 26(C).
    11. William Haskell & J. Shanthikumar & Z. Shen, 2013. "Optimization with a class of multivariate integral stochastic order constraints," Annals of Operations Research, Springer, vol. 206(1), pages 147-162, July.
    12. Keisuke Himoto, 2020. "Hierarchical Bayesian Modeling of Post‐Earthquake Ignition Probabilities Considering Inter‐Earthquake Heterogeneity," Risk Analysis, John Wiley & Sons, vol. 40(6), pages 1124-1138, June.
    13. Khakzad, Nima & Van Gelder, Pieter, 2018. "Vulnerability of industrial plants to flood-induced natechs: A Bayesian network approach," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 403-411.
    14. Xie, Shuyi & Huang, Zimeng & Wu, Gang & Luo, Jinheng & Li, Lifeng & Ma, Weifeng & Wang, Bohong, 2024. "Combining precursor and Cloud Leaky noisy-OR logic gate Bayesian network for dynamic probability analysis of major accidents in the oil depots," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    15. Seth Guikema, 2020. "Artificial Intelligence for Natural Hazards Risk Analysis: Potential, Challenges, and Research Needs," Risk Analysis, John Wiley & Sons, vol. 40(6), pages 1117-1123, June.
    16. Braga, Joaquim A.P. & Andrade, António R., 2021. "Multivariate statistical aggregation and dimensionality reduction techniques to improve monitoring and maintenance in railways: The wheelset component," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    17. Wang, Yuhao & Gao, Yi & Liu, Yongming & Ghosh, Sayan & Subber, Waad & Pandita, Piyush & Wang, Liping, 2021. "Bayesian-entropy gaussian process for constrained metamodeling," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    18. Liu, Shengli & Liang, Yongtu, 2021. "Statistics of catastrophic hazardous liquid pipeline accidents," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    19. Muhammad Sajjad & Zulfiqar Ali & Mirza Waleed, 2023. "Has Pakistan learned from disasters over the decades? Dynamic resilience insights based on catastrophe progression and geo-information models," 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. 117(3), pages 3021-3042, July.
    20. Peng Yang & Zhiping Chen, 2023. "Optimal Private Health Insurance Contract towards the Joint Interests of a Policyholder and an Insurer," Mathematics, MDPI, vol. 11(10), pages 1-28, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:riskan:v:39:y:2019:i:9:p:1930-1948. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1111/(ISSN)1539-6924 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.