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.

Citation
Yadav, R., Huser, R., Opitz, T., & Lombardo, L. (2023). 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. https://doi.org/10.1093/jrsssc/qlad077

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.

Publisher
Oxford University Press (OUP)

Journal
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS

DOI
10.1093/jrsssc/qlad077

arXiv
2205.09908

Additional Links
https://academic.oup.com/jrsssc/advance-article/doi/10.1093/jrsssc/qlad077/7272776

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2023-09-25 06:36:54
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