In landslide susceptibility modeling, the selection of the mapping units is a very relevant topic both in terms of geomorphological adequacy and suitability of the models and final maps. In this paper, a test to integrate pixels and slope units is presented. MARS (Multivariate Adaptive Regression Splines) modeling was applied to assess landslide susceptibility based on a 12 predictors and a 1608 cases database. A pixel-based model was prepared and the scores zoned into 10 different types of slope units, obtained by differently combining two half-basin (HB) and four landform classification (LCL) coverages. The predictive performance of the 10 models were then compared to select the best performing one, whose prediction image was finally modified to consider also the propagation stage. The results attest integrating HB with LCL as more performing than using simple HB classification, with a very limited loss in predictive performance with respect to the pixel-based model.

在滑坡敏感性建模中，就地貌学上的适宜性以及模型和最终地图的适用性而言，映射单位的选择是一个非常相关的主题。在本文中，提出了一种集成像素和斜率单位的测试。基于12个预测变量和1608个案例数据库，将MARS（多元自适应回归样条线）建模应用于评估滑坡敏感性。准备了一个基于像素的模型，并将得分分为10种不同类型的坡度单位，分别通过将两个半流域（HB）和四个地形分类（LCL）覆盖率进行组合获得。然后将这10个模型的预测性能进行比较，以选择性能最佳的模型，最后将其预测图像进行修改以考虑传播阶段。