Rainfall-induced landslides represent one of the most critical natural hazards affecting railway infrastructure in Italy, where complex geological settings and increasing climate-driven extremes challenge the reliability of transport services. This paper presents the SANF-RFI system, a national-scale early warning and decision-support platform developed through the collaboration between Rete Ferroviaria Italiana (RFI) and the Italian National Research Council – Institute for Geo-Hydrological Protection (CNR-IRPI). SANF-RFI adapts the national landslide early warning framework (SANF) to railway-specific requirements. It integrates near-real-time and forecast precipitation data with territorial susceptibility, railway exposure models, and quality-controlled rainfall observations. The system provides probabilistic estimates of rainfall-induced landslide triggering at the level of railway segments and sections, explicitly accounting for uncertainty related to rainfall measurement, spatial representativeness, and short-term forecast variability. After describing the system architecture, data flows, and probabilistic algorithms, the paper illustrates an operational application along the Marradi–Faenza railway line, where SANF-RFI enabled safe and flexible traffic management under severe hydro-meteorological conditions. The experience demonstrates how scientifically grounded early warning tools can enhance infrastructure resilience while maintaining essential railway services.
Murgia et al. (Fri,) studied this question.