Résumé
Risk assessment of structures subjected to flow-like natural hazards, such as mud/debris flows, lahars, or avalanches, are commonly based on propagation models. Numerical models require significant computational effort to predict output quantities, such as run-off lengths, velocities, or impact forces. In this study, Bayesian inversion is applied to a well-documented case in order to calibrate the governing parameters of the propagation model on kinematic measurements. As this technique is heavy in terms of model evaluations, it is performed with the help of a surrogate model. To ensure the accuracy of this surrogate model, various meta-modeling techniques are compared in the case of a small experimental design. Finally, a propagation model is built on the best point estimate resulting from the calibration. The results of this model are compared to the actual measurements of the studied case in order to demonstrate the applicability of the presented approach.