Joint modelling of landslide counts and sizes using spatial marked point processes with sub-asymptotic mark distributions - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement Access content directly
Journal Articles Journal of the Royal Statistical Society: Series C Applied Statistics Year : 2023

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

Abstract

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 and versions

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

Identifiers

Cite

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⟩
11 View
0 Download

Altmetric

Share

Gmail Facebook X LinkedIn More