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Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster

机译：使用INLA的多点泥石流滑坡基于点过程的建模：在2009年墨西拿灾难中的应用

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We develop a stochastic modeling approach based on spatial point processes of log-Gaussian Cox type for a collection of around 5000 landslide events provoked by a precipitation trigger in Sicily, Italy. Through the embedding into a hierarchical Bayesian estimation framework, we can use the Integrated Nested Laplace Approximation methodology to make inference and obtain the posterior estimates. Several mapping units are useful to partition a given study area in landslide prediction studies. These units hierarchically subdivide the geographic space from the highest grid-based resolution to the stronger morphodynamic-oriented slope units. Here we integrate both mapping units into a single hierarchical model, by treating the landslide triggering locations as a random point pattern. This approach diverges fundamentally from the unanimouslyudused presence-absence structure for areal units since we focus on modeling the expected landslide count jointly within the two mapping units. Predicting this landslide intensity provides more detailed and complete information as compared to the classically used susceptibility mapping approach based on relative probabilities. To illustrate the model's versatility, we compute absolute probability maps of landslide occurrences and check its predictive power over space. While the landslide community typically produces spatial predictive models for landslides only in the sense that covariates are spatially distributed, no actual spatial dependence has been explicitly integrated so far for landslide susceptibility. Our novel approach features a spatial latent effect defined at the slope unit level, allowing us to assess the spatial influence that remains unexplained by the covariates in the model.
机译：我们基于对数-高斯考克斯类型的空间点过程开发了一种随机建模方法，用于收集意大利西西里岛的一次降水触发引发的大约5000次滑坡事件。通过嵌入到分层贝叶斯估计框架中，我们可以使用集成嵌套拉普拉斯近似方法进行推理并获得后验估计。在滑坡预测研究中，几个测绘单位可用于划分给定的研究区域。这些单位将地理空间从基于网格的最高分辨率细分为更强的面向形态动力学的斜率单位。在这里，通过将滑坡触发位置视为随机点模式，我们将两个映射单元都集成到单个层次模型中。这种方法从根本上不同于区域单元的一致未使用的存在/缺失结构，因为我们集中在两个映射单元内共同对期望的滑坡计数进行建模。与基于相对概率的经典磁化率映射方法相比，预测此滑坡强度将提供更详细和完整的信息。为了说明该模型的多功能性，我们计算了滑坡发生的绝对概率图，并检查了其在空间上的预测能力。虽然滑坡群落通常仅在协变量在空间上分布的意义上生成滑坡的空间预测模型，但到目前为止，对于滑坡敏感性，还没有明确整合任何实际的空间依赖性。我们的新颖方法具有在坡度单位级别定义的空间潜在效应，使我们能够评估模型中协变量无法解释的空间影响。

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