Volume 11 Issue 6
Dec.  2021
Turn off MathJax
Article Contents
Andrew Deelstra, David Bristow. Characterizing Uncertainty in City-Wide Disaster Recovery through Geospatial Multi-Lifeline Restoration Modeling of Earthquake Impact in the District of North Vancouver[J]. International Journal of Disaster Risk Science, 2020, 11(6): 807-820. doi: 10.1007/s13753-020-00323-5
Citation: Andrew Deelstra, David Bristow. Characterizing Uncertainty in City-Wide Disaster Recovery through Geospatial Multi-Lifeline Restoration Modeling of Earthquake Impact in the District of North Vancouver[J]. International Journal of Disaster Risk Science, 2020, 11(6): 807-820. doi: 10.1007/s13753-020-00323-5

Characterizing Uncertainty in City-Wide Disaster Recovery through Geospatial Multi-Lifeline Restoration Modeling of Earthquake Impact in the District of North Vancouver

doi: 10.1007/s13753-020-00323-5
Funds:

Thanks to the District of North Vancouver, North Shore Emergency Management, Defence Research and Development Canada, and the Geological Survey of Canada for their contributions that made this project possible, as well as the anonymous reviewers whose feedback was greatly appreciated.

  • Available Online: 2021-12-25
  • Publish Date: 2021-12-25
  • Restoring lifeline services to an urban neighborhood impacted by a large disaster is critical to the recovery of the city as a whole. Since cities are comprised of many dependent lifeline systems, the pattern of the restoration of each lifeline system can have an impact on one or more others. Due to the often uncertain and complex interactions between dense lifeline systems and their individual operations at the urban scale, it is typically unclear how different patterns of restoration will impact the overall recovery of lifeline system functioning. A difficulty in addressing this problem is the siloed nature of the knowledge and operations of different types of lifelines. Here, a city-wide, multi-lifeline restoration model and simulation are provided to address this issue. The approach uses the Graph Model for Operational Resilience, a data-driven discrete event simulator that can model the spatial and functional cascade of hazard effects and the pattern of restoration over time. A novel case study model of the District of North Vancouver is constructed and simulated for a reference magnitude 7.3 earthquake. The model comprises municipal water and wastewater, power distribution, and transport systems. The model includes 1725 entities from within these sectors, connected through 6456 dependency relationships. Simulation of the model shows that water distribution and wastewater treatment systems recover more quickly and with less uncertainty than electric power and road networks. Understanding this uncertainty will provide the opportunity to improve data collection, modeling, and collaboration with stakeholders in the future.
  • loading
  • Applied Technology Council. 1985. ATC-13: Earthquake damage evaluation data for California. Redwood City, CA: Applied Technology Council.
    Berke, P.R., J. Kartez, and D. Wenger. 1993. Recovery after disaster: Achieving sustainable development, mitigation and equity. Disasters 17(2): 93–109.
    Bristow, D.N. 2019. How spatial and functional dependencies between operations and infrastructure leads to resilient recovery. Journal of Infrastructure Systems 25(2): Article 04019011.
    Bristow, D.N., and Hay, A.H. 2017. Graph model for probabilistic resilience and recovery planning of multi-infrastructure systems. Journal of Infrastructure Systems 23(3): Article 04016039.
    Cavdaroglu, B., E. Hammel, J.E. Mitchell, T.C. Sharkey, and W.A. Wallace. 2013. Integrating restoration and scheduling decisions for disrupted interdependent infrastructure systems. Annals of Operations Research 203(1): 279–294.
    DNV (The District of North Vancouver). 2015. When the ground shakes: A plain language companion study. https://www.dnv.org/sites/default/files/edocs/when-the-ground-shakes.pdf. Accessed 22 Mar 2019.
    Duffey, R.B. 2019. Power restoration prediction following extreme events and disasters. International Journal of Disaster Risk Science 10(1): 134–148.
    FEMA (Federal Emergency Management Agency). 2011. Hazus—MH 2.1 earthquake model technical manual. Washington, DC: FEMA. https://www.fema.gov/media-library-data/20130726-1820-25045-6286/hzmh2_1_eq_tm.pdf. Accessed 3 Jan 2019.
    Ganin, A.A., M. Kitsak, D. Marchese, J.M. Keisler, T. Seager, and I. Linkov. 2017. Resilience and efficiency in transportation networks. Science Advances 3(12): 1–9.
    Haimes, Y.Y. 2009. On the complex definition of risk: A systems-based approach. Risk Analysis 29(12): 1647–1654.
    He, F., and J. Nwafor. 2017. Gas pipeline recovery from disruption using multi-objective optimization. In Proceedings of 2017 IEEE International Symposium on Technologies for Homeland Security, 25–26 April 2017, Waltham, MA, USA, 378–383. Piscataway, NJ: Institute of Electrical and Electronics Engineers.
    Henry, D., and J. Emmanuel Ramirez-Marquez. 2012. Generic metrics and quantitative approaches for system resilience as a function of time. Reliability Engineering and System Safety 99: 114–122.
    Hu, X.B., M. Wang, T. Ye, and P. Shi. 2016. A new method for resource allocation optimization in disaster reduction and risk governance. International Journal of Disaster Risk Science 7(2): 138–150.
    Journeay, J.M., F. Dercole, D. Mason, M. Westin, J.A. Prieto, C.L. Wagner, N.L. Hastings, S.E. Chang, et al. 2015. A profile of earthquake risk for the district of North Vancouver, British Columbia. Geological Survey of Canada, Open File 7677. http://ftp.geogratis.gc.ca/pub/nrcan_rncan/publications/ess_sst/296/296256/of_7677.pdf. Accessed 22 Mar 2019.
    Khatavkar, P., and L.W. Mays. 2019. Optimization-simulation model for real-time pump and valve operation of water distribution systems under critical conditions. Urban Water Journal 16(1): 45–55.
    Loggins, R.A., and W.A. Wallace. 2015. Rapid assessment of hurricane damage and disruption to interdependent civil infrastructure systems. Journal of Infrastructure Systems 21(4): Article 04015005.
    Lubashevskiy, V., T. Kanno, and K. Furuta. 2014. Resource redistribution method for short-term recovery of society after large-scale disasters. Advances in Complex Systems 17(5): Article 1450026.
    Miles, S.B. 2018. Participatory disaster recovery simulation modeling for community resilience planning. International Journal of Disaster Risk Science 9(4): 519–529.
    Muriel-Villegas, J.E., K.C. Alvarez-Uribe, C.E. Patiño-Rodríguez, and J.G. Villegas. 2016. Analysis of transportation networks subject to natural hazards—Insights from a Colombian case. Reliability Engineering and System Safety 152: 151–165.
    Nateghi, R. 2018. Multi-dimensional infrastructure resilience modeling: An application to hurricane-prone electric power distribution systems. IEEE Access 6: 13478–13489.
    Onuma, H., K. Joo, and S. Managi. 2017. Household preparedness for natural disasters: Impact of disaster experience and implications for future disaster risks in Japan. International Journal of Disaster Risk Reduction 21: 148–158.
    Ouyang, M. 2014. Review on modeling and simulation of interdependent critical infrastructure systems. Reliability Engineering and System Safety 121: 43–60.
    Ouyang, M., and Z. Wang. 2015. Resilience assessment of interdependent infrastructure systems: With a focus on joint restoration modeling and analysis. Reliability Engineering and System Safety 141: 74–82.
    Pagano, A., C. Sweetapple, R. Farmani, R. Giordano, and D. Butler. 2019. Water distribution networks resilience analysis: A comparison between graph theory-based approaches and global resilience analysis. Water Resources Management 33(8): 2925–2940.
    Rodríguez, H., W. Donner, and J.E. Trainor (eds.). 2018. Handbooks of sociology and social research handbook of disaster research, 2nd edn. Switzerland: Springer International.
    Rubin, C.B., M.D. Saperstein, and D.G. Barbee. 1985. Community recovery from a major natural disaster. Boulder, CO: Institute of Behavioral Science, University of Colorado.
    Setola, R., V. Rosato, E. Kyriakides, and E. Rome (eds.). 2016. Managing the complexity of critical infrastructures: A modelling and simulation approach. Switzerland: Springer International.
    Statistics Canada. 2017. North Vancouver, DM (Census Subdivision), British Columbia and Greater Vancouver, RD (Census Division), British Columbia (Table). Census profile. In 2016 Census. Statistics Canada Catalogue No. 98-316-X2016001. Ottawa: Statistics Canada.
    Stergiopoulos, G., P. Kotzanikolaou, M. Theocharidou, G. Lykou, and D. Gritzalis. 2016. Time-based critical infrastructure dependency analysis for large-scale and cross-sectoral failures. International Journal of Critical Infrastructure Protection 12: 46–60.
    Sullivan, J.L., D.C. Novak, L. Aultman-Hall, and D.M. Scott. 2010. Identifying critical road segments and measuring system-wide robustness in transportation networks with isolating links: A link-based capacity-reduction approach. Transportation Research Part A: Policy and Practice 44(5): 323–336.
    Tran, H.T., M. Balchanos, J.C. Domerçant, and D.N. Mavris. 2017. A framework for the quantitative assessment of performance-based system resilience. Reliability Engineering and System Safety 158: 73–84.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (580) PDF downloads(0) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return