Integrating GNSS and Explainable AI for rainfall thresholds in large landslide monitoring

Landslides are a widespread hazard in Italy, and Early Warning Systems (LEWS) help mitigate this risk through non-structural measures. Recent advances in monitoring and data analysis have improved LEWS but identifying spatially and temporally variable triggering factors remains challenging. Integrating low-cost GNSS with precipitation networks can enhance system reliability. In this study, a continuous early warning system focusing on rainfall as a triggering factor was applied to a complex deep-seated landslide in the Carnic Alps, north-eastern Italy. The Cazzaso landslide monitoring system, installed in 2016 by the CRS (OGS) in collaboration with the Regional Civil Protection, continuously collects displacement data from 12 GPS and 2 GNSS stations. Time series of displacement and precipitation data from two rain gauges were analyzed to identify landslide reactivation events using a velocity threshold—a novel approach that provides valuable insights for updating LEWS protocols. The Cazzaso landslide was found to be primarily rainfall-triggered, leading to the application of empirical Intensity–Duration (I–D) rainfall thresholds for early warning. Validation showed limited reliability, likely due to the landslide’s complex geometry and depth, which are not fully captured by simple statistical methods. To address this, a Random Forest (RF) model combined with Explainable AI (XAI) techniques was employed. Out-of-Bag Error (OOBE) assessed variable importance, and Partial Dependence Plots (PDPs) illustrated their influence. The analysis identified 8-day cumulative rainfall as the most effective predictor of landslide reactivation, enabling the definition of more reliable thresholds for the GNSS-based warning system. This integrated approach improves the operational effectiveness of LEWS and can be adapted to evaluate short- and long-term rainfall impacts in diverse geological and climatic contexts. While site-specific, the methodology provides a transferable framework for other landslide-prone areas.

Acknowledgements

This study was carried out within the RETURN Extended Partnership and received funding from the European Union Next-GenerationEU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005)