Landslide prediction based on jointly analysis of triggering and predisposing factors

Regional-scale prediction of shallow landslides is essential for operational early-warning systems. Rainfall-duration thresholds and susceptibility mapping are the most commonly used approaches for defining triggering conditions and predisposing factors, respectively. In this study, we investigate a joint approach that combines triggering and predisposing factors.

This study is conducted in the Southeast of France, which was severely affected by the Storm Alex, a millennial return period rainfall event, in 2020. It relies on a retrospective analysis of 1600 shallow landslides recorded in the study area. A random forest approach is applied to quantify the relative importance of landslide geomorphological factors, i.e. geology, parameters derived from Digital Elevation Model (slope angle, aspect, profile curvature…), and several landslide hydrometeorological factors, including the cumulative 1-day, 5-day, 10-day, 30-day and 90-day antecedent rainfall. The significance of the factors is analysed, as well as the performance of the prediction, for normal and extreme rainfall events. This study constitutes a step towards a real-time landslide prediction model, to be then integrated within an early warning system.