Regional-scale spatiotemporal landslide probability assessment through machine learning and potential applications for operational warning systems: a case study in Kvam (Norway)
Citation
Nocentini, N., Rosi, A., Piciullo, L., Liu, Z., Segoni, S., Fanti, R. (2024). Regional-scale spatiotemporal landslide probability assessment through machine learning and potential applications for operational warning systems: a case study in Kvam (Norway). Landslides, 21(10): 2369–2387. Link to paper
Abstract
This study introduces an innovative dynamic (i.e., space–time-dependent) application of the random forest algorithm for evaluating landslide hazard (i.e., spatiotemporal probability of landslide occurrence). An area in Norway has been chosen as the case study because of the availability of a comprehensive, spatially, and temporally explicit rainfall-induced landslide inventory. The study area encompasses the locality of Kvam in Norway, which experienced two major rainfall events in 2011 and 2013, resulting in more than 100 landslides. The applied methodology is based on the inclusion of dynamic variables, such as cumulative rainfall, snowmelt, and their seasonal variability, as model inputs, together with traditional static parameters such as lithology and morphologic attributes. The model's performance was validated through spatial and temporal cross-validation schemes, demonstrating its ability to predict landslide occurrence with high accuracy. The results highlight the potential of integrating dynamic environmental variables into machine learning models for improved spatiotemporal landslide forecasting and the development of operational early warning systems.