Landslide susceptibility corresponds to the probability of landslide occurrence across a given geographic space. This probability is usually estimated by using a binary classifier which is informed of landslide presence/absence data and associated landscape characteristics. Here, we consider the Italian national landslide inventory to prepare slope-unit based landslide susceptibility maps. These maps are prepared for the eight types of mass movements existing in the inventory, (Complex, Deep Seated Gravitational Slope Deformation, Diffused Fall, Fall, Rapid Flow, Shallow, Slow Flow, Translational) and we build one susceptibility map for each type. The analysis – carried out by using a Bayesian version of a Generalized Additive Model with a multiple intercept for each Italian region – revealed that the inventory may have been compiled with different levels of detail. This would be consistent with the dataset being assembled from twenty sub–inventories, each prepared by different administrations of the Italian regions. As a result, this spatial heterogeneity may lead to biased national–scale susceptibility maps. On the basis of these considerations, we further analyzed the national database to confirm or reject the varying quality hypothesis on the basis of the model equipped with multiple regional intercepts. For each landslide type, we then tried to build unbiased susceptibility models by removing regions with a poor landslide inventory from the calibration stage, and used them only as a prediction target of a simulation routine. We analyzed the resulting eight maps finding out a congruent dominant pattern in the Alpine and Apennine sectors.

The whole procedure is implemented in R–INLA. This allowed to examine fixed (linear) and random (nonlinear) effects from an interpretative standpoint and produced a full prediction equipped with an estimated uncertainty.

We propose this overall modeling pipeline for any landslide datasets where a significant mapping bias may influence the susceptibility pattern over space.

滑坡敏感性对应于在给定地理空间内发生滑坡的概率。这个概率通常通过使用二元分类器来估计，该分类器被告知滑坡存在/不存在数据和相关的景观特征。在这里，我们考虑意大利国家滑坡清单，以准备基于斜坡单元的滑坡敏感性图。这些地图是为清单中存在的八种类型的质量运动（复杂、深部重力斜率变形、扩散下落、下落、快速流动、浅水、慢速流动、平移）准备的，我们为每种类型建立一个敏感性地图。该分析使用贝叶斯版本的广义加法模型对每个意大利地区进行了多次截距，显示该清单可能已经以不同的详细程度进行了编译。这将与由 20 个子清单组合而成的数据集一致，每个子清单由意大利地区的不同主管部门准备。因此，这种空间异质性可能导致有偏见的国家尺度敏感性地图。基于这些考虑，我们进一步分析了国家数据库，以在配备多个区域截距的模型的基础上确认或拒绝不同的质量假设。对于每种滑坡类型，我们尝试通过从校准阶段移除滑坡库存较差的区域来建立无偏敏感性模型，并且仅将它们用作模拟程序的预测目标。我们分析了生成的八张地图，发现了阿尔卑斯山和亚平宁地区的一致主导模式。

整个过程在 R-INLA 中实现。这允许从解释的角度检查固定（线性）和随机（非线性）效应，并产生配备估计不确定性的完整预测。

我们为任何滑坡数据集提出了这种整体建模管道，其中显着的映射偏差可能会影响空间敏感性模式。