Articles | Volume 22, issue 6
https://doi.org/10.5194/nhess-22-2031-2022
https://doi.org/10.5194/nhess-22-2031-2022
Research article
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14 Jun 2022
Research article | Highlight paper |  | 14 Jun 2022

Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland

Cristina Pérez-Guillén, Frank Techel, Martin Hendrick, Michele Volpi, Alec van Herwijnen, Tasko Olevski, Guillaume Obozinski, Fernando Pérez-Cruz, and Jürg Schweizer

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Cited articles

Baggi, S. and Schweizer, J.: Characteristics of wet-snow avalanche activity: 20 years of observations from a high alpine valley (Dischma, Switzerland), Nat. Hazards, 50, 97–108, https://doi.org/10.1007/s11069-008-9322-7, 2009. a
Bavay, M. and Egger, T.: MeteoIO 2.4.2: a preprocessing library for meteorological data, Geosci. Model Dev., 7, 3135–3151, https://doi.org/10.5194/gmd-7-3135-2014, 2014. a
Bowler, N. E.: Explicitly accounting for observation error in categorical verification of forecasts, Mon. Weather Rev., 134, 1600–1606, https://doi.org/10.1175/MWR3138.1, 2006. a
Brabec, B. and Meister, R.: A nearest-neighbor model for regional avalanche forecasting, Ann. Glaciol., 32, 130–134, https://doi.org/10.3189/172756401781819247, 2001. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. a
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Executive editor
The paper could have a strong impact in the entire Alpine region, where avalanche forecasting is a critical issue to manage
Short summary
A fully data-driven approach to predicting the danger level for dry-snow avalanche conditions in Switzerland was developed. Two classifiers were trained using a large database of meteorological data, snow cover simulations, and danger levels. The models performed well throughout the Swiss Alps, reaching a performance similar to the current experience-based avalanche forecasts. This approach shows the potential to be a valuable supplementary decision support tool for assessing avalanche hazard.
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