Less than 10 meters deep, shallow landslides are rapidly moving and strongly dangerous slides. In the present work, the probabilistic distribution of the landslide detachment points within a valley is modelled as a spatial Poisson point process, whose intensity depends on geophysical predictors according to a generalized additive model. Modelling the intensity with a generalized additive model jointly allows to obtain good predictive performance and to preserve the interpretability of the effects of the geophysical predictors on the intensity of the process. We propose a novel workflow, based on Random Forests, to select the geophysical predictors entering the model for the intensity. In this context, the statistically significant effects are interpreted as activating or stabilizing factors for landslide detachment. In order to guarantee the transferability of the resulting model, training, validation, and test of the algorithm are performed on mutually disjoint valleys in the Alps of Lombardy (Italy). Finally, the uncertainty around the estimated intensity of the process is quantified via semiparametric bootstrap.

翻译：浅层滑坡是指深度小于10米、移动迅速且危险性极高的滑坡体。本研究将山谷内滑坡滑脱点的概率分布建模为空间泊松点过程，其强度通过广义可加模型依赖于地球物理预测因子。采用广义可加模型对强度进行建模，既能获得良好的预测性能，又能保持地球物理预测因子对过程强度影响的可解释性。我们提出一种基于随机森林的新工作流程，用于筛选进入强度模型的地球物理预测因子。在此框架下，统计显著的影响被解释为滑坡滑脱的激活因子或稳定因子。为保证所得模型的迁移性，算法训练、验证与测试均在意大利伦巴第阿尔卑斯山脉中相互独立的山谷进行。最后，通过半参数自举法量化了过程强度估计值的不确定性。