Comparative analysis of conventional and machine learning techniques for rainfall threshold evaluation under complex geological conditions

The Emilia-Romagna Region in Italy faces significant challenges due to landslide hazards. With over 80,000 landslides identified in its mountainous regions, some areas see more than a quarter of their land impacted. Despite the generally slow nature of these landslides, they pose a considerable economic burden. For instance, in 2019, the region allocated 1 million euros for immediate safety measures, and it's estimated that an additional 80 million euros are needed to complete safety plans. This makes Emilia-Romagna one of the most landslide-prone areas globally. Factors like the region's geological makeup, increased land use, and climate change are exacerbating the issue. It's becoming evident that emergency measures alone are insufficient, and proactive prevention strategies are essential. Key efforts include better forecasting of rain-induced slope instabilities and predicting reactivations of dormant landslides and new failures. However, the unpredictable nature of landslides makes these goals challenging. The primary aim of this study is to create AI models to predict landslides in Emilia-Romagna, leveraging 75 years of data collected by the University of Bologna in partnership with the Regional Agency for Civil Protection and the Geological Survey of Emilia-Romagna. Various methods like Bayesian analysis, Neural Networks, XGBoost, TPOT, Random Forest, LDA, QDA, and Linear Regression have been employed. The findings suggest that landslides in this region are primarily driven by rainfall during the event and its location, while prior rainfall seems less critical. The research also found that after a dry summer, a rainfall event of 90-100 mm is typically needed to trigger a landslide, a threshold that decreases later in the year. The best algorithm had an F2 score test result of 0.6, meaning it could correctly predict a true positive (rainfall causing landslide) every 3 positive instances and correctly predict a true negative (rainfall not causing landslide) 95.5% of the time.