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A Systematic Approach of Global Sensitivity Analysis and Its Application to a Model for the Quantification of Resilience of Interconnected Critical Infrastructures

Author

Listed:
  • Xing Liu

    (Chair on Systems Science and the Energetic Challenge, Foundation Electricité de France (EDF), Centrale Supélec, 91190 Gif-sur-Yvette, France)

  • Enrico Zio

    (Centre de Recherche sur les Risques et les Crises (CRC), MINES Paris-PSL Université Paris, 06904 Sophia Antipolis, France
    Department of Energy, Politecnico di Milano, 20133 Milan, Italy)

  • Emanuele Borgonovo

    (Department of Decision Sciences, Bocconi Institute for Data Science and Analytics, Bocconi University, 20136 Milan, Italy)

  • Elmar Plischke

    (Institute of Disposal Research, Clausthal University of Technology, 38678 Clausthal-Zellerfeld, Germany)

Abstract

We consider a model for the resilience analysis of interconnected critical infrastructures (ICIs) that describes the dependencies among the subsystems within the ICIs and their time-varying behavior. The model response is a function of uncertain inputs comprising ICIs design parameters and failure magnitudes of vulnerable elements in the system, etc. In this methodological paper, we present a systematic approach based on an innovative blend of methods to perform a sensitivity analysis for identifying the most relevant variables affecting the system resilience at different stages, during a disruptive event. The methods considered include the following: the use of the graphical representation of Cusunoro curves for a visualization of the impact of an input on the resilience metric and an understanding of whether the associated dependence is monotonic, increasing, or decreasing; the introduction of an ensemble of indicators related to different properties of the resilience metric to allow the prioritization of variable importance and avoid false negatives, meaning to regard a variable as non-influential when, instead, it plays a relevant role in the determination of the model response; the calculation of first-order variance-based sensitivity indices to have an appreciation on the relevance of interactions when inputs are independent; and a data approach to visually identify relevant second-order interactions. All the sensitivity methods considered are performed on a provided sample, and do not require additional model evaluations. They allow the analyst to post-process the data to extract, simultaneously, several desirable insights. The systematic approach proposed to apply these methods allows us to identify the model input variables and parameters that are not very relevant, while it enables the identification of the relevant ones which allows prioritizing interventions on the vulnerable elements of the system for its resilience at different stages during a disruptive event. Given the methodological nature of the work, a simplified infrastructure model describing an interconnected gas network and electric power grid is taken as case study: this allows us to show that the approach is straightforward to understand and implement, and the results obtained show the usefulness of the approach in providing meaningful insights that can be used by stakeholders and decision makers to inform strategies for the improvement of system resilience. By the application of the simplified ICIs model to the case study, it is shown that the approach can be straightforwardly implemented to identify the most relevant variables on system resilience and obtain the most important subsystems. The key factors which affect system resilience in multiple initial failures scenarios are found; this allows us to identify the key resilience improvement measurements, and their priorities.

Suggested Citation

  • Xing Liu & Enrico Zio & Emanuele Borgonovo & Elmar Plischke, 2024. "A Systematic Approach of Global Sensitivity Analysis and Its Application to a Model for the Quantification of Resilience of Interconnected Critical Infrastructures," Energies, MDPI, vol. 17(8), pages 1-24, April.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:8:p:1823-:d:1373787
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    References listed on IDEAS

    as
    1. Borgonovo, E., 2007. "A new uncertainty importance measure," Reliability Engineering and System Safety, Elsevier, vol. 92(6), pages 771-784.
    2. E. Borgonovo & S. Tarantola & E. Plischke & M. D. Morris, 2014. "Transformations and invariance in the sensitivity analysis of computer experiments," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(5), pages 925-947, November.
    3. Ronald L. Iman & Mark E. Johnson & Charles C. Watson, 2005. "Uncertainty Analysis for Computer Model Projections of Hurricane Losses," Risk Analysis, John Wiley & Sons, vol. 25(5), pages 1299-1312, October.
    4. Emanuele Borgonovo & Gordon B. Hazen & Elmar Plischke, 2016. "A Common Rationale for Global Sensitivity Measures and Their Estimation," Risk Analysis, John Wiley & Sons, vol. 36(10), pages 1871-1895, October.
    5. Tarantola, S. & Kopustinskas, V. & Bolado-Lavin, R. & Kaliatka, A. & Ušpuras, E. & Vaišnoras, M., 2012. "Sensitivity analysis using contribution to sample variance plot: Application to a water hammer model," Reliability Engineering and System Safety, Elsevier, vol. 99(C), pages 62-73.
    6. Plischke, Elmar, 2012. "An adaptive correlation ratio method using the cumulative sum of the reordered output," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 149-156.
    7. Manel Baucells & Emanuele Borgonovo, 2013. "Invariant Probabilistic Sensitivity Analysis," Management Science, INFORMS, vol. 59(11), pages 2536-2549, November.
    8. E. E. Koks & M. Bočkarjova & H. de Moel & J. C. J. H. Aerts, 2015. "Integrated Direct and Indirect Flood Risk Modeling: Development and Sensitivity Analysis," Risk Analysis, John Wiley & Sons, vol. 35(5), pages 882-900, May.
    9. Barry Anderson & Emanuele Borgonovo & Marzio Galeotti & Roberto Roson, 2014. "Uncertainty in Climate Change Modeling: Can Global Sensitivity Analysis Be of Help?," Risk Analysis, John Wiley & Sons, vol. 34(2), pages 271-293, February.
    10. Jean-Claude Fort & Thierry Klein & Nabil Rachdi, 2016. "New sensitivity analysis subordinated to a contrast," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(15), pages 4349-4364, August.
    11. J. C. Helton & F. J. Davis, 2002. "Illustration of Sampling‐Based Methods for Uncertainty and Sensitivity Analysis," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 591-622, June.
    12. Ronald L. Iman & Stephen C. Hora, 1990. "A Robust Measure of Uncertainty Importance for Use in Fault Tree System Analysis," Risk Analysis, John Wiley & Sons, vol. 10(3), pages 401-406, September.
    13. Elmar Plischke & Emanuele Borgonovo, 2020. "Fighting the Curse of Sparsity: Probabilistic Sensitivity Measures From Cumulative Distribution Functions," Risk Analysis, John Wiley & Sons, vol. 40(12), pages 2639-2660, December.
    14. Xuefei Lu & Alessandro Rudi & Emanuele Borgonovo & Lorenzo Rosasco, 2020. "Faster Kriging: Facing High-Dimensional Simulators," Operations Research, INFORMS, vol. 68(1), pages 233-249, January.
    15. Mark Strong & Jeremy E. Oakley & Jim Chilcott, 2012. "Managing structural uncertainty in health economic decision models: a discrepancy approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(1), pages 25-45, January.
    16. Sumeet R. Patil & H. Christopher Frey, 2004. "Comparison of Sensitivity Analysis Methods Based on Applications to a Food Safety Risk Assessment Model," Risk Analysis, John Wiley & Sons, vol. 24(3), pages 573-585, June.
    17. Zio, Enrico, 2016. "Challenges in the vulnerability and risk analysis of critical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 137-150.
    18. Ronald L. Iman & Mark E. Johnson & Charles C. Watson, 2005. "Sensitivity Analysis for Computer Model Projections of Hurricane Losses," Risk Analysis, John Wiley & Sons, vol. 25(5), pages 1277-1297, October.
    19. Plischke, Elmar, 2010. "An effective algorithm for computing global sensitivity indices (EASI)," Reliability Engineering and System Safety, Elsevier, vol. 95(4), pages 354-360.
    20. Damblin, Guillaume & Ghione, Alberto, 2021. "Adaptive use of replicated Latin Hypercube Designs for computing Sobol’ sensitivity indices," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    21. Matieyendou Lamboni & Moez Sanaa & Fanny Tenenhaus‐Aziza, 2014. "Sensitivity Analysis for Critical Control Points Determination and Uncertainty Analysis to Link FSO and Process Criteria: Application to Listeria monocytogenes in Soft Cheese Made from Pasteurized Mil," Risk Analysis, John Wiley & Sons, vol. 34(4), pages 751-764, April.
    22. Andrea Saltelli, 2002. "Sensitivity Analysis for Importance Assessment," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 579-590, June.
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