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Modelling the spatio-temporal repartition of right-truncated data: an application to avalanche runout altitudes in Hautes-Savoie

机译:右截断数据的时空分区建模:在上萨瓦省雪崩跳动高度中的应用

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摘要

In this paper, we propose a novel approach for generating avalanche hazard maps based on the spatial dependence of avalanche runout altitudes. The right- truncated data are described with a Bayesian hierarchical model in which the spatio- temporal process is assumed to be the sum of independent spatial and temporal terms. Topography is roughly taken into account according to valley altitude and path exposition, and the spatial dependence is modelled with a Matern covariance function. An application is performed to the Haute-Savoie region, French Alps. A spatial dependence in runout altitudes is identified, and an effective range of about 10 km is inferred. The temporal trend extracted highlights the increase of avalanche runout altitudes from 1955, attributed to both anthropogenic factors and climate warming. In a cross validation scheme, spatial predictions are provided on undocumented paths using kriging equations. All in all, although our model is unable to take into account small topographic features, it is a first-ever approach that produces very encouraging results. It could be enhanced in future work by incorporating a numerical physically-based code into the modelling.
机译:在本文中,我们提出了一种基于雪崩跳动高度的空间依赖性生成雪崩危险图的新方法。右截断的数据使用贝叶斯分层模型描述,其中时空过程被假定为独立的空间和时间项之和。根据山谷的海拔高度和路径说明,大致考虑了地形,并使用Matern协方差函数对空间依赖性进行了建模。在法国阿尔卑斯山的上萨瓦省地区进行了申请。确定了跳动高度的空间依赖性,并推断出约10 km的有效范围。提取的时间趋势突显了自1955年以来雪崩跳动海拔的增加,这归因于人为因素和气候变暖。在交叉验证方案中,使用克里金方程在未记录的路径上提供空间预测。总而言之,尽管我们的模型无法考虑小的地形特征,但这是有史以来第一个产生令人鼓舞结果的方法。通过将基于物理的数字代码合并到建模中,可以在以后的工作中增强它。

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