IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v100y2020i1d10.1007_s11069-019-03799-7.html
   My bibliography  Save this article

Analysis of the susceptibility of interdependent infrastructures using fuzzy input–output inoperability model: the case of flood hazards in Tehran

Author

Listed:
  • Sina Samimi

    (Islamic Azad University)

  • Sadoullah Ebrahimnejad

    (Islamic Azad University)

  • Mohammad Mojtahedi

    (The University of New South Wales)

Abstract

Advancement in technology has contributed to increment in complexity of systems and infrastructures. Furthermore, it has complicated the management of systems to deal with natural hazards. Input–output inoperability model (IIM) is a simple method to characterize the impacts of natural hazards on interconnected infrastructures. In this paper, the impacts of a flood hazard on six critical infrastructures in Tehran metropolitan have been assessed by using IIM. The computational results show that energy and transportation infrastructures are the most influencing infrastructures, while emergency services and healthcare infrastructures are the most influenced infrastructures. All data required to evaluate this case study have been collected using questionnaires and converted to fuzzy interdependency values. To increase decision-making power, the developed fuzzy matrix has been arranged for different risk levels (from absolutely optimistic to absolutely pessimistic) and confidence levels (from absolutely confident to absolutely non-confident). Afterward, the interdependency matrix has been deffuzified, and inoperability of infrastructures has been calculated by the IIM for seven different initial conditions. Finally, a sensitivity analysis has been conducted to incorporate the risk levels and confidence levels to determine values of inoperability under the above-mentioned conditions. The ranking for both of the influencing and the influenced infrastructures has also been provided. This ranking helps decision makers to manage natural hazard risks effectively by appropriate resource allocation. It also helps to realize the interdependencies among infrastructures and to determine the inoperability of infrastructures before natural hazards. This would help decision makers to mitigate the risk and prepare the society well in advance.

Suggested Citation

  • Sina Samimi & Sadoullah Ebrahimnejad & Mohammad Mojtahedi, 2020. "Analysis of the susceptibility of interdependent infrastructures using fuzzy input–output inoperability model: the case of flood hazards in Tehran," 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. 100(1), pages 69-88, January.
    • Handle: RePEc:spr:nathaz:v:100:y:2020:i:1:d:10.1007_s11069-019-03799-7
    DOI: 10.1007/s11069-019-03799-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-019-03799-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-019-03799-7?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Setola, Roberto & De Porcellinis, Stefano & Sforna, Marino, 2009. "Critical infrastructure dependency assessment using the input–output inoperability model," International Journal of Critical Infrastructure Protection, Elsevier, vol. 2(4), pages 170-178.
    2. Pu Jiang & Yacov Y. Haimes, 2004. "Risk Management for Leontief‐Based Interdependent Systems," Risk Analysis, John Wiley & Sons, vol. 24(5), pages 1215-1229, October.
    3. ASIMOPOLOS, Laurentiu & ASIMOPOLOS, Adrian-Aristide & ASIMOPOLOS, Natalia-Silvia, 2018. "The Role Of Interdependencies Between Critical Infrastructures In Rural Development," Annals of Spiru Haret University, Economic Series, Universitatea Spiru Haret, vol. 18(2), pages 63-81.
    4. Oliva, Gabriele & Panzieri, Stefano & Setola, Roberto, 2011. "Fuzzy dynamic input–output inoperability model," International Journal of Critical Infrastructure Protection, Elsevier, vol. 4(3), pages 165-175.
    Full references (including those not matched with items on IDEAS)

    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. Amine El Haimar & Joost R. Santos, 2014. "Modeling Uncertainties in Workforce Disruptions from Influenza Pandemics Using Dynamic Input‐Output Analysis," Risk Analysis, John Wiley & Sons, vol. 34(3), pages 401-415, March.
    2. Rahmatallah Poudineh and Tooraj Jamasb, 2017. "Electricity Supply Interruptions: Sectoral Interdependencies and the Cost of Energy Not Served for the Scottish Economy," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    3. Olaf Jonkeren & Bogdan Dorneanu & Georgios Giannopoulos & David Ward, 2012. "Regional economic assessment of Critical Infrastructure failure in the EU: A combined systems engineering and economic model," ERSA conference papers ersa12p92, European Regional Science Association.
    4. Klein, Peter & Klein, Fabian, 2019. "Dynamics of interdependent critical infrastructures – A mathematical model with unexpected results," International Journal of Critical Infrastructure Protection, Elsevier, vol. 24(C), pages 69-77.
    5. Wenping Xu & Zongjun Wang & Liu Hong & Ligang He & Xueguang Chen, 2015. "The uncertainty recovery analysis for interdependent infrastructure systems using the dynamic inoperability input–output model," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(7), pages 1299-1306, May.
    6. Niknejad, Ali & Petrovic, Dobrila, 2016. "A fuzzy dynamic Inoperability Input–output Model for strategic risk management in Global Production Networks," International Journal of Production Economics, Elsevier, vol. 179(C), pages 44-58.
    7. Lam, C.Y. & Tai, K., 2018. "Modeling infrastructure interdependencies by integrating network and fuzzy set theory," International Journal of Critical Infrastructure Protection, Elsevier, vol. 22(C), pages 51-61.
    8. Baghersad, Milad & Zobel, Christopher W., 2015. "Economic impact of production bottlenecks caused by disasters impacting interdependent industry sectors," International Journal of Production Economics, Elsevier, vol. 168(C), pages 71-80.
    9. Heracleous, Constantinos & Kolios, Panayiotis & Panayiotou, Christos G. & Ellinas, Georgios & Polycarpou, Marios M., 2017. "Hybrid systems modeling for critical infrastructures interdependency analysis," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 89-101.
    10. Joost R. Santos, 2006. "Inoperability input‐output modeling of disruptions to interdependent economic systems," Systems Engineering, John Wiley & Sons, vol. 9(1), pages 20-34, March.
    11. Wang, Yanhui & Bi, Lifeng & Lin, Shuai & Li, Man & Shi, Hao, 2017. "A complex network-based importance measure for mechatronics systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 180-198.
    12. Alana M. Weir & Thomas M. Wilson & Mark S. Bebbington & Sarah Beaven & Teresa Gordon & Craig Campbell-Smart & Stuart Mead & James H. Williams & Roger Fairclough, 2024. "Approaching the challenge of multi-phase, multi-hazard volcanic impact assessment through the lens of systemic risk: application to Taranaki Mounga," 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. 120(10), pages 9327-9360, August.
    13. Rehak, David & Senovsky, Pavel & Hromada, Martin & Lovecek, Tomas & Novotny, Petr, 2018. "Cascading Impact Assessment in a Critical Infrastructure System," International Journal of Critical Infrastructure Protection, Elsevier, vol. 22(C), pages 125-138.
    14. Olaf Jonkeren & Ivano Azzini & Luca Galbusera & Stavros Ntalampiras & Georgios Giannopoulos, 2015. "Analysis of Critical Infrastructure Network Failure in the European Union: A Combined Systems Engineering and Economic Model," Networks and Spatial Economics, Springer, vol. 15(2), pages 253-270, June.
    15. Jian Jin & Haoran Zhou, 2023. "A Demand-Side Inoperability Input–Output Model for Strategic Risk Management: Insight from the COVID-19 Outbreak in Shanghai, China," Sustainability, MDPI, vol. 15(5), pages 1-22, February.
    16. Hassan Al-Zarooni & Hamdi Bashir, 2020. "An integrated ISM fuzzy MICMAC approach for modeling and analyzing electrical power system network interdependencies," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(6), pages 1204-1226, December.
    17. Zhou, Shenghua & Yang, Yifan & Ng, S. Thomas & Xu, J. Frank & Li, Dezhi, 2020. "Integrating data-driven and physics-based approaches to characterize failures of interdependent infrastructures," International Journal of Critical Infrastructure Protection, Elsevier, vol. 31(C).
    18. Cottafava, Dario & Gastaldo, Michele & Quatraro, Francesco & Santhiá, Cristina, 2022. "Modeling economic losses and greenhouse gas emissions reduction during the COVID-19 pandemic: Past, present, and future scenarios for Italy," Economic Modelling, Elsevier, vol. 110(C).
    19. Fang, Zhixiang & Shaw, Shih-Lung & Tu, Wei & Li, Qingquan & Li, Yuguang, 2012. "Spatiotemporal analysis of critical transportation links based on time geographic concepts: a case study of critical bridges in Wuhan, China," Journal of Transport Geography, Elsevier, vol. 23(C), pages 44-59.
    20. Linn Svegrup & Jonas Johansson & Henrik Hassel, 2019. "Integration of Critical Infrastructure and Societal Consequence Models: Impact on Swedish Power System Mitigation Decisions," Risk Analysis, John Wiley & Sons, vol. 39(9), pages 1970-1996, September.

    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:spr:nathaz:v:100:y:2020:i:1:d:10.1007_s11069-019-03799-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.