Abstract Snow avalanches pose a significant and escalating natural hazard, especially in climate change and global warming times, demanding for advanced predictive capabilities to safeguard lives and infrastructure in mountainous regions. Solution methods, even augmented by machine learning, frequently contend with inherent data complexities, imbalanced event occurrences, strict time constraints, and conflicting safety and economic risks to be minimized and traded off. This paper introduces the Avalanche Risk Prediction Intelligent System (ARPIS) implementing a novel data-driven approach designed to address these challenges in a comprehensive, intelligent and adaptive framework. ARPIS proposes an incremental, feedback-oriented methodology that continuously collects and manages data, improves the model, and infers live avalanche risk prediction, eventually triggering alerts to support decision making. The ARPIS suitability and effectiveness are demonstrated by a full-fledged case study on a specific dataset from the high-risk mountainous terrain of the Susa Valley in the Italian Alps.
Bui et al. (Sun,) studied this question.