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Uncertainty analysis in natural and environmental risk assessments using extra-probabilistic uncertainty approaches: description of concepts through practical examples
Convener: Jeremy Rohmer  | Co-Convener: Jean-Charles Manceau 
Wed, 26 Apr, 17:30–19:00

Uncertainty analysis is an unavoidable task of risk assessments either for natural hazards like landslides, earthquakes, floods, volcanoes, etc., or for environmental issues like groundwater or soil contamination. When dealing with uncertainties, two facets should be considered as outlined by several authors. The first facet corresponds to “aleatoric uncertainty” (also named “randomness” or “intrinsic variability”) and arises from the natural variability owing to either heterogeneity or to the random character of natural processes (i.e. stochasticity). A common example of aleatoric uncertainty is the variability in weather. The second facet corresponds to “epistemic uncertainty” and arises when dealing with “partial ignorance” i.e. when facing “vague, incomplete or imprecise information” such as limited databases and observations or “imperfect” modelling.

Although the probabilistic setting has been used in a broad range of different applications, the use of probabilities as a tool to represent epistemic uncertainties has often been criticized in situations where the available data are imprecise, scarce, incomplete, vague, qualitative, etc. In such highly uncertain situations, the challenge is to formulate appropriate mathematical tools and models in a quantitative manner, on the one hand, accounting for all data and pieces of information, but, on the other hand, without introducing unwarranted assumptions. Therefore, to overcome the shortcomings of the pure probabilistic setting, several alternative representation methods have been developed: probability boxes, possibility distributions, Dempster-Shafer structures, etc.

The purpose of the short course is to describe how these new tools can be used to handle epistemic uncertainty for the different stages: uncertainty representation; propagation, sensitivity analysis and decision-making. This will be supported by real cases taken from landslide, earthquake, flood and health risk assessment (Baudrit et al., 2005; Rohmer & Baudrit, 2011; Rohmer & Verdel, 2014; Pedroni et al., 2013). The illustrations will be performed using R package “HYRISK”, which is freely available upon request.


Baudrit C, Guyonnet D, Dubois D (2005) Post-processing the hybrid method for addressing uncertainty in risk assessments. Journal of Environmental Engineering 131:1750-1754

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., & Baudrit, C. (2011). The use of the possibility theory to investigate the epistemic uncertainties within scenario-based earthquake risk assessments. Natural hazards, 56(3), 613-632.

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