Integrating a deep-averaged fluid propagation model in a hierarchical Bayesian framework for avalanche predetermination: Monte Carlo calibration, predictive simulations and pressure computations
2008
Eckert, Nicolas | Naaim, Mohamed | Parent, Eric | Érosion torrentielle, neige et avalanches (UR ETGR (ETNA)) ; Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF)
[Departement_IRSTEA]RE [TR1_IRSTEA]RIE / ALPRISK
Afficher plus [+] Moins [-]anglais. For a few years, methods inspired by hydrological modelling associated with Monte Carlo simulations have been proposed to address the issue of avalanche predetermination. Enough fictitious events are generated in order to compute the probability distributions of the variables of interest, mainly runout distances and pressure fields. A questionable assumption is the choice of appropriate distributions for the different variables, which generally comes rather from practical reasons than from physical considerations. Moreover, the propagation model must match a compromise between the precision of the description of the avalanche flow, computation times and inference feasibility. Bayesian modelling is an interesting option to overcome the technical difficulties associated with the probabilistic calibration of a complex numerical model on real data.
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