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Sensitivity analysis in presence of model uncertainty and correlated inputs

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  • Jacques, Julien
  • Lavergne, Christian
  • Devictor, Nicolas

Abstract

The first motivation of this work is to take into account model uncertainty in sensitivity analysis (SA). We present with some examples, a methodology to treat uncertainty due to a mutation of the studied model. Development of this methodology has highlighted an important problem, frequently encountered in SA: how to interpret sensitivity indices when random inputs are non-independent? This paper suggests a strategy for the problem of SA of models with non-independent random inputs. We propose a new application of the multidimensional generalization of classical sensitivity indices, resulting from group sensitivities (sensitivity of the output of the model to a group of inputs), and describe an estimation method based on Monte-Carlo simulations. Practical and theoretical applications illustrate the interest of this method.

Suggested Citation

  • Jacques, Julien & Lavergne, Christian & Devictor, Nicolas, 2006. "Sensitivity analysis in presence of model uncertainty and correlated inputs," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1126-1134.
    • Handle: RePEc:eee:reensy:v:91:y:2006:i:10:p:1126-1134
    DOI: 10.1016/j.ress.2005.11.047
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    Citations

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    Cited by:

    1. Iooss, Bertrand & Ribatet, Mathieu, 2009. "Global sensitivity analysis of computer models with functional inputs," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1194-1204.
    2. López-Benito, Alfredo & Bolado-Lavín, Ricardo, 2017. "A case study on global sensitivity analysis with dependent inputs: The natural gas transmission model," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 11-21.
    3. Salacinska, K. & El Serafy, G.Y. & Los, F.J. & Blauw, A., 2010. "Sensitivity analysis of the two dimensional application of the Generic Ecological Model (GEM) to algal bloom prediction in the North Sea," Ecological Modelling, Elsevier, vol. 221(2), pages 178-190.
    4. Dimov, I. & Georgieva, R., 2010. "Monte Carlo algorithms for evaluating Sobol’ sensitivity indices," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(3), pages 506-514.
    5. Venelin Todorov & Slavi Georgiev & Ivan Georgiev & Snezhinka Zaharieva & Ivan Dimov, 2023. "Optimizing Air Pollution Modeling with a Highly-Convergent Quasi-Monte Carlo Method: A Case Study on the UNI-DEM Framework," Mathematics, MDPI, vol. 11(13), pages 1-14, June.
    6. Venelin Todorov & Ivan Dimov, 2022. "Innovative Digital Stochastic Methods for Multidimensional Sensitivity Analysis in Air Pollution Modelling," Mathematics, MDPI, vol. 10(12), pages 1-14, June.
    7. Haro Sandoval, Eduardo & Anstett-Collin, Floriane & Basset, Michel, 2012. "Sensitivity study of dynamic systems using polynomial chaos," Reliability Engineering and System Safety, Elsevier, vol. 104(C), pages 15-26.
    8. Mara, Thierry A. & Tarantola, Stefano, 2012. "Variance-based sensitivity indices for models with dependent inputs," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 115-121.
    9. Mirko Ginocchi & Ferdinanda Ponci & Antonello Monti, 2021. "Sensitivity Analysis and Power Systems: Can We Bridge the Gap? A Review and a Guide to Getting Started," Energies, MDPI, vol. 14(24), pages 1-59, December.
    10. Lin, Yan-Hui & Yam, Richard C.M., 2017. "Uncertainty importance measures of dependent transition rates for transient and steady state probabilities," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 402-409.
    11. Blagojević, Nikola & Didier, Max & Stojadinović, Božidar, 2022. "Quantifying component importance for disaster resilience of communities with interdependent civil infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    12. Broto, Baptiste & Bachoc, François & Depecker, Marine & Martinez, Jean-Marc, 2019. "Sensitivity indices for independent groups of variables," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 163(C), pages 19-31.
    13. Rogeau, A. & Girard, R. & Kariniotakis, G., 2017. "A generic GIS-based method for small Pumped Hydro Energy Storage (PHES) potential evaluation at large scale," Applied Energy, Elsevier, vol. 197(C), pages 241-253.
    14. Labopin-Richard T. & Gamboa F. & Garivier A. & Iooss B., 2016. "Bregman superquantiles. Estimation methods and applications," Dependence Modeling, De Gruyter, vol. 4(1), pages 1-33, March.
    15. Marie Hermant & Olivier Blanchard & Guillaume Perouel & Guillaume Boulanger & Mathilde Merlo & Virginie Desvignes, 2018. "Environmental Exposure of the Adult French Population to Permethrin," Risk Analysis, John Wiley & Sons, vol. 38(4), pages 853-865, April.

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