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Article Dans Une Revue Journal of the Royal Statistical Society: Series C Applied Statistics Année : 2023

Joint modelling of landslide counts and sizes using spatial marked point processes with sub-asymptotic mark distributions

Résumé

To accurately quantify landslide hazard in a region of Turkey, we develop new marked point-process models within a Bayesian hierarchical framework for the joint prediction of landslide counts and sizes. We leverage mark distributions justified by extreme-value theory, and specifically propose ‘sub-asymptotic’ distributions to flexibly model landslide sizes from low to high quantiles. The use of intrinsic conditional autoregressive priors, and a customised adaptive Markov chain Monte Carlo algorithm, allow for fast fully Bayesian inference. We show that sub-asymptotic mark distributions provide improved predictions of large landslide sizes, and use our model for risk assessment and hazard mapping.

Dates et versions

hal-04228509 , version 1 (04-10-2023)

Identifiants

Citer

Rishikesh Yadav, Raphaël Huser, Thomas Opitz, Luigi Lombardo. Joint modelling of landslide counts and sizes using spatial marked point processes with sub-asymptotic mark distributions. Journal of the Royal Statistical Society: Series C Applied Statistics, 2023, ⟨10.1093/jrsssc/qlad077⟩. ⟨hal-04228509⟩
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