Automated prediction of wet-snow avalanche activity in Switzerland

Avalanche hazard forecasting is essential to reduce the risk for people and infrastructure in mountain areas. Among the different types of avalanches, wet-snow avalanches are particularly challenging to predict due to the poor understanding of their release mechanism. We therefore trained a random forest model to predict wet-snow avalanche activity based on weather and snow measurements and downstream SNOWPACK simulations provided by automated weather stations. The model was trained on a database covering 20 years of avalanche observations (avalanche type, size, location, slope aspect) in the context of operational avalanche forecasting in Switzerland. The prediction performance (F1-score: harmonic mean between recall and precision) for wet-snow avalanche active days is around 76% (recall: 73%, precision: 80%), and is 99% for days with no activity.  The model not only well reproduced the onset, but also the end of wet-snow avalanche periods. Operational testing during winter 2021-2022 allow to evaluate differences in model performance between nowcast derived from meteorological measurements and forecast from numerical weather prediction models. Overall, the results are promising and are an important step forward a more reliable forecast of wet-snow avalanche activity.