Reply on RC1

Abstract

Landslide hazard models aim at mitigating landslide impact by providing probabilistic forecasting, and the accuracy of these models hinges on landslide databases for model training and testing. Landslide databases at times lack information on the underlying triggering mechanism, making these inventories almost unusable in hazard models. We developed a Python-based library, landsifier, that contains three different Machine-Learning frameworks for assessing the likely triggering mechanisms of individual landslides or entire inventories based on landslide geometry. Two of these methods only use the 2D landslide planforms, and the third utilizes the 3D shape of landslides relying on an underlying Digital Elevation Model (DEM). The base method extracts geometric properties of landslide polygons as a feature space for the shallow learner—Random Forest (RF). An alternative method extracts topological properties of 3D landslides through Topological Data Analysis (TDA) and then feeds these properties as a feature space to the Random Forest classifier. The last framework relies on landslide-planform images as an input for the deep learning algorithm—Convolutional Neural Network (CNN). We tested all three interchangeable methods on several inventories with known triggers spread over the Japanese archipelago. To demonstrate the effectiveness of developed methods, we used two testing configurations. The first configuration merges all the available data for the k-fold cross-validation, whereas the second configuration excludes one inventory during the training phase to use as the sole testing inventory. Classification accuracies for different testing schemes vary between 70 % and 95 %. Finally, we implemented the three methods on an inventory without any triggering information to showcase a real-world application.