In risk assessment, the exposure component describes the elements exposed to the natural hazards and susceptible to damage or loss, while the vulnerability component defines the likelihood to incur damage or loss conditional on a given level of hazard intensity. In this article, we propose a novel adaptive approach to exposure modeling which exploits Dirichlet-Multinomial Bayesian updating to implement the incremental assimilation of sparse in situ survey data into probabilistic models described by compositions (proportions). This methodology is complemented by the introduction of a custom spatial aggregation support based on variable-resolution Central Voronoidal Tessellations. The proposed methodology allows for a more consistent integration of empirical observations, typically from engineering surveys, into large-scale models that can also efficiently exploit expert-elicited knowledge. The resulting models are described in a probabilistic framework, and as such allow for a more thorough analysis of the underlying uncertainty. The proposed approach is applied and discussed in five countries in Central Asia.

中文翻译：

---

用于风险应用的住宅暴露和脆弱性的可变分辨率概率建模

在风险评估中，暴露部分描述了暴露于自然灾害中并容易受到损害或损失的要素，而脆弱性部分则定义了以给定的灾害强度水平为条件发生损害或损失的可能性。在本文中，我们提出了一种新的曝光建模自适应方法，该方法利用 Dirichlet-Multinomial Bayesian 更新将稀疏的原位调查数据增量同化为由成分（比例）描述的概率模型。这种方法是通过引入基于可变分辨率中央 Voronoidal Tessellations 的自定义空间聚合支持来补充的。提议的方法允许更一致地整合经验观察，通常来自工程调查，转化为大规模模型，这些模型也可以有效地利用专家引出的知识。由此产生的模型在概率框架中进行描述，因此可以对潜在的不确定性进行更彻底的分析。提议的方法在中亚的五个国家得到应用和讨论。