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Landslide size matters: A new data-driven, spatial prototype
Engineering Geology ( IF 7.4 ) Pub Date : 2021-07-24 , DOI: 10.1016/j.enggeo.2021.106288
Luigi Lombardo , Hakan Tanyas , Raphaël Huser , Fausto Guzzetti , Daniela Castro-Camilo

The standard definition of landslide hazard requires the estimation of where, when (or how frequently) and how large a given landslide event may be. The geoscientific community involved in statistical models has addressed the component pertaining to how large a landslide event may be by introducing the concept of landslide-event magnitude scale. This scale, which depends on the planimetric area of the given population of landslides, in analogy to the earthquake magnitude, has been expressed with a single value per landslide event. As a result, the geographic or spatially-distributed estimation of how large a population of landslide may be when considered at the slope scale, has been disregarded in statistically-based landslide hazard studies. Conversely, the estimation of the landslide extent has been commonly part of physically-based applications, though their implementation is often limited to very small regions.

In this work, we initially present a review of methods developed for landslide hazard assessment since its first conception decades ago. Subsequently, we introduce for the first time a statistically-based model able to estimate the planimetric area of landslides aggregated per slope units. More specifically, we implemented a Bayesian version of a Generalized Additive Model where the maximum landslide size per slope unit and the sum of all landslide sizes per slope unit are predicted via a Log-Gaussian model. These “max” and “sum” models capture the spatial distribution of (aggregated) landslide sizes. We tested these models on a global dataset expressing the distribution of co-seismic landslides due to 24 earthquakes across the globe. The two models we present are both evaluated on a suite of performance diagnostics that suggest our models suitably predict the aggregated landslide extent per slope unit. In addition to a complex procedure involving variable selection and a spatial uncertainty estimation, we built our model over slopes where landslides triggered in response to seismic shaking, and simulated the expected failing surface over slopes where the landslides did not occur in the past.

What we achieved is the first statistically-based model in the literature able to provide information about the extent of the failed surface across a given landscape. This information is vital in landslide hazard studies and should be combined with the estimation of landslide occurrence locations. This could ensure that governmental and territorial agencies have a complete probabilistic overview of how a population of landslides could behave in response to a specific trigger. The predictive models we present are currently valid only for the 25 cases we tested. Statistically estimating landslide extents is still at its infancy stage. Many more applications should be successfully validated before considering such models in an operational way. For instance, the validity of our models should still be verified at the regional or catchment scale, as much as it needs to be tested for different landslide types and triggers. However, we envision that this new spatial predictive paradigm could be a breakthrough in the literature and, in time, could even become part of official landslide risk assessment protocols.



中文翻译:

滑坡大小很重要:一种新的数据驱动的空间原型

滑坡灾害的标准定义要求估计给定滑坡事件的发生地点、时间(或频率)和规模。参与统计模型的地球科学界已经通过引入滑坡事件震级的概念来解决与滑坡事件可能有多大有关的组成部分。该比例取决于给定滑坡群的面积,类似于地震震级,已用每个滑坡事件的单一值表示。因此,在基于统计的滑坡灾害研究中,在考虑斜坡尺度时,对滑坡人口可能有多大的地理或空间分布估计已被忽略。相反,滑坡范围的估计通常是基于物理的应用的一部分,

在这项工作中,我们首先回顾了几十年前首次提出滑坡灾害评估方法。随后,我们首次引入了一种基于统计的模型,能够估计每个坡度单元聚合的滑坡面积。更具体地说,我们实施了广义加性模型的贝叶斯版本,其中通过对数高斯模型预测每个斜坡单元的最大滑坡尺寸和每个斜坡单元的所有滑坡尺寸的总和。这些“最大”和“总和”模型捕捉(聚合)滑坡大小的空间分布。我们在表示全球 24 次地震引起的同震滑坡分布的全球数据集上测试了这些模型。我们提出的两个模型都在一套性能诊断上进行了评估,这表明我们的模型可以适当地预测每个斜坡单元的总滑坡范围。除了涉及变量选择和空间不确定性估计的复杂程序外,我们还在响应地震震动而触发滑坡的斜坡上建立了我们的模型,并在过去没有发生滑坡的斜坡上模拟了预期的破坏面。

我们取得的是文献中第一个基于统计的模型,能够提供有关给定景观中失效表面范围的信息。这些信息在滑坡灾害研究中至关重要,应与滑坡发生位置的估计相结合。这可以确保政府和地区机构对滑坡群如何响应特定触发因素有一个完整的概率概览。我们提供的预测模型目前仅对我们测试的 25 个案例有效。统计估计滑坡范围仍处于起步阶段。在以可操作的方式考虑此类模型之前,应该成功验证更多应用程序。例如,我们模型的有效性仍应在区域或流域尺度上进行验证,尽可能多地测试不同的滑坡类型和触发因素。然而,我们设想这种新的空间预测范式可能是文献中的一个突破,甚至可能成为官方滑坡风险评估协议的一部分。

更新日期:2021-07-27
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