Insights Gained from the Review of Landslide Susceptibility Assessment Studies in Italy - Samuele Segoni; Rajendran Shobha Ajin; Nicola Nocentini; Riccardo Fanti (Remote Sensing)
We conducted a systematic literature review of 105 landslide susceptibility studies in Italy from 1980 to 2023, retrieved from the Scopus database. We discovered that Italian researchers primarily focus on rainfall-induced landslides (86.67% of the articles), especially shallow and fast movements (60%), with 72% of studies conducted at the local scale, while regional and national-level studies are rare. The most common data sources include remote sensing images validated by field surveys and official data portals at the national or regional level. Data splitting usually follows a 70:30 ratio and 24 modelling techniques were identified, with logistic regression being historically prevalent, although machine learning methods have rapidly gained popularity. Italian studies used 97 predisposing factors, with slope angle (98.09%), lithology (89.52%), land use/land cover (78.09%), and aspect (77.14%) being the most employed. This review also identifies and discusses a few less-used factors, like soil sealing, rainfall, NDVI, and proximity to faults, which showed promising results in experimental studies. Predisposing factors are generally selected by expert judgment, but methods for forward factors selection and collinearity tests are becoming more common. This review synthesizes current knowledge, pinpointing gaps, highlighting emerging methodologies, and suggesting future research directions for better integration of susceptibility studies with landslide risk management.