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Modelling intra-dependencies to assess road network resilience to natural hazards

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Abstract

Estimating the resilience of a road network (one of the essential critical infrastructures in times of crisis) to natural hazards is crucial in achieving the goals of disaster risk reduction (DRR). This study proposes a new predictive method to implement, in an operational way, the concept of resilience by exploring the robustness of the road network in Baalbek-Hermel Governorate (Lebanon) in order to predict its future behavior in response to natural hazards occurrence. The proposed methodology consists of a predictive-spatial-analytic approach based on geospatial numerical models combined with an R-NetSwan function for modeling and simulating critical infrastructures. The results show that Baalbek-Hermel’s road network is moderately resilient since it reaches a total loss of connectivity when nearly 60% of its critical nodes are blocked or damaged. Earthquakes proved to be the most disruptive hazards of this network, threatening the connectivity, starting its first damaged nodes, and causing the highest percentages of connectivity loss (70%). The novelty of this method lies in utilizing network analysis to reveal roads resilience to different natural hazards and serve several operational targets: revealing the defects of the road network for improvement or the construction of new detours, as well as allowing the first aid services to better visualize these weaknesses and to better prepare themselves. This study facilitates the implementation of a proactive approach to DRR and the protection of CI networks for better crisis response and much more effective evacuation plans.

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Source: CNRS (2017)

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Acknowledgements

This research is part of a PhD thesis funded by the National Council of Scientific Research-Lebanon (CNRS-L). Authors thank CNRS-L for their funding. Thanks is also extended to editor and reviewers for their valuable comments in leveraging the scientific quality of work.

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Authors and Affiliations

  1. National Council for Scientific Research, Remote Sensing Center, Natural Hazard, Beirut, Lebanon

    Rita Der Sarkissian & Chadi Abdallah

  2. Laboratoire d’Economie Orléans (LEO), Université d’Orléans, Orléans, France

    Rita Der Sarkissian & Jean-Marc Zaninetti

  3. Department of Physics, American University of Beirut, Riad El-Solh 1107, 2020, Beirut, Lebanon

    Sara Najem

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  1. Rita Der Sarkissian
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Correspondence to Chadi Abdallah.

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Der Sarkissian, R., Abdallah, C., Zaninetti, JM. et al. Modelling intra-dependencies to assess road network resilience to natural hazards. Nat Hazards 103, 121–137 (2020). https://doi.org/10.1007/s11069-020-03962-5

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