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
 | Highlight paper
 | 
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

Viewed

Total article views: 4,111 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,039 989 83 4,111 139 66 50
  • HTML: 3,039
  • PDF: 989
  • XML: 83
  • Total: 4,111
  • Supplement: 139
  • BibTeX: 66
  • EndNote: 50
Views and downloads (calculated since 22 Nov 2021)
Cumulative views and downloads (calculated since 22 Nov 2021)

Viewed (geographical distribution)

Total article views: 4,111 (including HTML, PDF, and XML) Thereof 3,910 with geography defined and 201 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 29 Jun 2024
Download
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.
Altmetrics
Final-revised paper
Preprint