Assessing the predictive capability of several machine learning algorithms to forecast snow avalanches using numerical weather prediction model in eastern Canada

Abstract. Snow avalanches are a serious threat to traffic in the northern Gaspésie region. In this study, we look at the development of different forecasting models using machine learning (ML), based on snow avalanche events recorded by Quebec's Ministry of Transportation (MTMQ), meteorological data from the Cap-Madeleine station and Environment Canada weather forecast data. The models were trained and tested on Train and Test datasets with meteorological and weather forecasts recorded at the Meteorological Station. Unsupervised learning models were compared to expert models where only 4 variables were selected with avalanche expertise in mind, yielding similar results in prediction. The ML models were then tested in a realistic forecasting context over the year 2019 with weather data from a forecasting station (Hindcast) and with forecast data over 24 h and 48 h (GEMLAM 24 h). The LR and RF models show that model performance can match or exceed that of current forecasting tools, enhancing hazard anticipation while maintaining a user-friendly framework suitable for real-time application. In conclusion, recommendations on forecast-based operational procedures are proposed.