Dealing with imperfect knowledge in natural hazard assessments: beyond classical probabilities and challenges

Managing multi-source data requires flexible approaches and tools to model many types of imperfections surrounding them and, and more braodly, to deal with uncertainties of multiple origins, namely aleatory (representing randomness) and epistemic uncertainty (related to imperfect knowledge). While the first origin can be adequately represented using classical probabilities, there is no simple, single answer for epistemic uncertainty. New theories of uncertainty based on "imprecise probabilities" have been developed in the literature to go beyond the systematic use of a single probabilistic law. In this communication, I analyze the application of these methods for quantifying uncertainty in various real-world cases of natural hazard assessment (earthquakes, floods, rockfalls) in terms of their advantages and disadvantages compared to the traditional probabilistic approach. On this basis, I draw lessons to support decision making under uncertainty and identify open questions and remaining challenges, in particular the integration of spatio-temporal geodata, the use of full process high-fidelity numerical models, and interfacing with AI-based approaches.

_{I acknowledge financial support of the French National Research Agency within the HOUSES project (grant N°ANR-22-CE56-0006).}