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Hybrid Methods For Addressing Uncertainty In RISK Assessments

hyriskR-package.Rd

Methods for addressing uncertainty in risk assessments using hybrid representations of uncertainty (probability distributions, fuzzy numbers, intervals, probability distributions with imprecise parameters). The uncertainty propagation procedure combines random sampling using Monte Carlo method with fuzzy interval analysis of Baudrit et al. (2007) <doi:10.1109/TFUZZ.2006.876720>. The sensitivity analysis is based on the pinching method of Ferson and Tucker (2006) <doi:10.1016/j.ress.2005.11.052>.

Details

This package provides tools for uncertainty analysis:

  • Create input uncertain variables represented by an interval, a possibility distribution (trapezoidal or triangular), a probability distribution (normal, lognormal, beta, triangle, Gumbel or user-defined), or an imprecise probability distribution.

  • Perform joint uncertainty propagation using IRS of Baudrit et al. (2007) or Hybrid algorithm described by Baudrit et al. (2006).

  • Perform uncertainty propagation when the random variables are represented by imprecise probabilities, i.e. probability distribution with ill-known parameters (Pedroni et al., 2013).

  • Improve the speed of uncertainty propagation via low discrepancy random sequences or surrogate-based methods (Lockwood et al., 2012).

  • Summarize the uncertan results in the form of a pair of lower and upper cumulative distribution functions CDFs.

  • Summarize the uncertainty in the form of an interval of exceedance probabilities, an interval of quantiles, or a global indicator corresponding to the area between the lower and upper CDFs.

  • Perform sensitivity analysis using a pinching approach (Ferson and Tucker, 2006) or regional approach (Rohmer and Verdel, 2014).

Author

Author: Jeremy Rohmer, Jean-Charles Manceau, Dominique Guyonnet, Faiza Boulahya Maintainer: Jeremy Rohmer <j.rohmer@brgm.fr>

References

  • Baudrit, C., Dubois, D., & Guyonnet, D. 2006. Joint propagation and exploitation of probabilistic and possibilistic information in risk assessment. IEEE transactions on fuzzy systems, 14(5), 593-608.

  • Baudrit, C., Guyonnet, D., Dubois, D. 2007. Joint propagation of variability and partial ignorance in a groundwater risk assessment. Journal of Contaminant Hydrology, 93: 72-84.

  • Ferson, S., & Tucker, W. T. (2006). Sensitivity analysis using probability bounding. Reliability Engineering & System Safety, 91(10), 1435-1442.

  • Lockwood, B., Anitescu, M., & Mavriplis, D. (2012). Mixed aleatory/epistemic uncertainty quantification for hypersonic flows via gradient-based optimization and surrogate models. In 50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition (p. 1254).

  • Pedroni, N., Zio, E., Ferrario, E., Pasanisi, A., & Couplet, M. 2013. Hierarchical propagation of probabilistic and non-probabilistic uncertainty in the parameters of a risk model. Computers & Structures, 126, 199-213.

  • Rohmer, J., & Verdel, T. (2014). Joint exploration of regional importance of possibilistic and probabilistic uncertainty in stability analysis. Computers and Geotechnics, 61, 308-315.