Articles | Volume 25, issue 4
https://doi.org/10.5194/nhess-25-1331-2025
https://doi.org/10.5194/nhess-25-1331-2025
Research article
 | 
08 Apr 2025
Research article |  | 08 Apr 2025

Assessing the performance and explainability of an avalanche danger forecast model

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

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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.
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