Assessing the performance and explainability of an avalanche danger forecast model

Cristina Pérez-Guillén, Frank Techel, Michele Volpi, and Alec van Herwijnen

Data sets

Weather, snowpack and danger ratings data for automated avalanche danger level predictions Cristina Pérez-Guillén et al. https://doi.org/10.16904/envidat.330

Model code and software

deapsnow_live_v1 Cristina Pérez-Guillén et al. https://gitlabext.wsl.ch/perezg/deapsnow_live_v1

Short summary

This study assesses the performance and explainability of a random-forest classifier for predicting dry-snow avalanche danger levels during initial live testing. The model achieved ∼ 70 % agreement with human forecasts, performing equally well in nowcast and forecast modes, while capturing the temporal dynamics of avalanche forecasting. The explainability approach enhances the transparency of the model's decision-making process, providing a valuable tool for operational avalanche forecasting.