This paper presents a modeling approach for probabilistic estimation of hurricane wind-induced damage to infrastructural assets. In our approach, we employ a Nonhomogeneous Poisson Process (NHPP) model for estimating spatially-varying probability distributions of damage as a function of hurricane wind field velocities. Specifically, we consider a physically-based, quadratic NHPP model for failures of overhead assets in electricity distribution systems. The wind field velocities are provided by Forecasts of Hurricanes using Large-Ensemble Outputs (FHLO), a framework for generating probabilistic hurricane forecasts. We use FHLO in conjunction with the NHPP model, such that the hurricane forecast uncertainties represented by FHLO are accounted for in estimating the probability distributions of damage. Furthermore, we evaluate the spatial variability and extent of hurricane damage under key wind field parameters (intensity, size, and asymmetries). By applying our approach to prediction of power outages (loss-of-service) in northwestern Florida due to Hurricane Michael (2018), we demonstrate a statistically significant relationship between outage rate and failure rate. Finally, we formulate parametric models that relate total damage and financial losses to the hurricane parameters of intensity and size. Overall, this paper's findings suggest that our approach is well-suited to jointly account for spatial variability and forecast uncertainty in the damage estimates, and is readily applicable to prediction of system loss-of-service due to the damage.

翻译：本文介绍了一种模型方法,用以对飓风风引起的基础设施资产损害进行概率估计。在我们的方法中,我们使用一种非同质波斯松进程模型,作为飓风风场速度的函数,来估计损害的空间变化概率分布。具体地说,我们考虑一种基于物理的、四端的NHPP模型,以弥补电力分配系统中间接资产失灵。风场速度由利用大增益产出(FHLO)预测飓风预报提供,这是产生概率性飓风预报的框架。我们结合NHPP模型使用FHLO模型来估计损害的空间变化概率分布。我们使用FHLO模型来估计损害的空间分布概率分布。此外,我们根据主要风场参数(强度、规模和不对称)评估飓风破坏的空间变化和程度。我们采用的方法预测佛罗里达西北部因飓风迈克尔(2018年)而导致的电力断流(服务损失),我们展示了在可适用性服务损失率和损失率参数之间具有重要统计意义的关系。最后,我们根据准确性估算的飓风系统损失率和损失率和总体损失率,我们用这个模型来评估飓风损失率。