Joint modelling of landslide counts and sizes using spatial marked point processes with sub-asymptotic mark distributions
针对土耳其某区域滑坡灾害，提出贝叶斯分层框架下的新标记点过程模型，联合预测滑坡次数与规模，采用极值理论推导的次渐近分布灵活建模滑坡规模，改进大滑坡预测，用于风险评估与灾害制图。
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.