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

Rishikesh Yadav, Raphaël Huser, Thomas Opitz, Luigi Lombardo

Research output: Contribution to journalArticlepeer-review

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.

Bibliographical note

KAUST Repository Item: Exported on 2023-09-26
Acknowledged KAUST grant number(s): OSR-CRG2020-4338
Acknowledgements: This publication is based on the work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-CRG2020-4338. The authors are grateful to the reviewers and the editors for their helpful comments and suggestions that improved the quality of the manuscript.

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint

Dive into the research topics of 'Joint modelling of landslide counts and sizes using spatial marked point processes with sub-asymptotic mark distributions'. Together they form a unique fingerprint.

Cite this