Articles | Volume 22, issue 6
https://doi.org/10.5194/nhess-22-2031-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-22-2031-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
14 Jun 2022
Research article | Highlight paper |
| 14 Jun 2022
| 14 Jun 2022
Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland
Cristina Pérez-Guillén
CORRESPONDING AUTHOR
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Frank Techel
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Martin Hendrick
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Michele Volpi
Swiss Data Science Center, ETH Zurich and EPFL, Zurich, Switzerland
Alec van Herwijnen
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Tasko Olevski
Swiss Data Science Center, ETH Zurich and EPFL, Zurich, Switzerland
Guillaume Obozinski
Swiss Data Science Center, ETH Zurich and EPFL, Zurich, Switzerland
Fernando Pérez-Cruz
Swiss Data Science Center, ETH Zurich and EPFL, Zurich, Switzerland
Department of Computer Science, ETH Zurich, Zurich, Switzerland
Jürg Schweizer
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
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Cited
36 citations as recorded by crossref.
- The EAWS matrix, a decision support tool to determine the regional avalanche danger level (Part A): conceptual development K. Müller et al. https://doi.org/10.5194/nhess-25-4503-2025
- Probability models to convert snowpack stability into the number of dry-snow avalanches in North Japan Y. Katsuyama et al. https://doi.org/10.1016/j.coldregions.2025.104480
- Early warning – international collaboration using map-based and user-centered approaches C. Lienert https://doi.org/10.1080/23729333.2025.2479429
- Changing drivers of regional large magnitude avalanche frequency throughout Colorado, USA E. Peitzsch et al. https://doi.org/10.5194/nhess-26-1059-2026
- Combining modelled snowpack stability with machine learning to predict avalanche activity L. Viallon-Galinier et al. https://doi.org/10.5194/tc-17-2245-2023
- Clustering simulated snow profiles to form avalanche forecast regions S. Horton et al. https://doi.org/10.5194/gmd-18-193-2025
- Development and evaluation of a method to identify potential release areas of snow avalanches based on watershed delineation C. Duvillier et al. https://doi.org/10.5194/nhess-23-1383-2023
- A neural network model for automated prediction of avalanche danger level V. Sharma et al. https://doi.org/10.5194/nhess-23-2523-2023
- Can model-based avalanche forecasts match the discriminatory skill of human danger-level forecasts? A comparison from Switzerland F. Techel et al. https://doi.org/10.5194/nhess-25-3333-2025
- A quantitative module of avalanche hazard – comparing forecaster assessments of storm and persistent slab avalanche problems with information derived from distributed snowpack simulations F. Herla et al. https://doi.org/10.5194/nhess-25-625-2025
- Empirical Study on the Conditions for Blowing Snow Events at Roads on High-Altitude Mountains L. Feng et al. https://doi.org/10.1007/s13351-025-4192-4
- Cooperative avalanche beacon localization using machine-learned geometric constraints and sequential regression-plane intersection T. Yonekura et al. https://doi.org/10.1016/j.coldregions.2026.105005
- A three-stage model pipeline predicting regional avalanche danger in Switzerland (RAvaFcast v1.0.0): a decision-support tool for operational avalanche forecasting A. Maissen et al. https://doi.org/10.5194/gmd-17-7569-2024
- Relating storm-snow avalanche instabilities to data collected from the Differential Emissivity Imaging Disdrometer (DEID) T. Morrison et al. https://doi.org/10.1016/j.coldregions.2023.103839
- Spatially continuous snow depth mapping by aeroplane photogrammetry for annual peak of winter from 2017 to 2021 in open areas L. Bührle et al. https://doi.org/10.5194/tc-17-3383-2023
- Towards deep-learning solutions for classification of automated snow height measurements (CleanSnow v1.0.2) J. Svoboda et al. https://doi.org/10.5194/gmd-18-1829-2025
- Prediction of natural dry-snow avalanche activity using physics-based snowpack simulations S. Mayer et al. https://doi.org/10.5194/nhess-23-3445-2023
- Selection of Significant Features for Snow Avalanche Forecasting K. Kaushik et al. https://doi.org/10.1007/s10666-025-10061-x
- Assessing the predictive capability of several machine learning algorithms to forecast snow avalanches using numerical weather prediction model in eastern Canada F. Gauthier et al. https://doi.org/10.5194/nhess-25-5033-2025
- Acoustic emission monitoring and analyses for avalanche release and slab fracturing events observed in Great Himalaya J. Kapil et al. https://doi.org/10.1080/10589759.2023.2208717
- Predicting avalanche danger in northern Norway using statistical models K. Eiselt & R. Graversen https://doi.org/10.5194/tc-19-1849-2025
- Impact of climate change on snow avalanche activity in the Swiss Alps S. Mayer et al. https://doi.org/10.5194/tc-18-5495-2024
- Climate change impacts on snow avalanche activity and related risks N. Eckert et al. https://doi.org/10.1038/s43017-024-00540-2
- Climate snow-avalanche linkage revealed by geomorphological, historical and tree-ring records in the central Spanish Pyrenees O. Franco-Ramos et al. https://doi.org/10.1016/j.coldregions.2026.104819
- Automating avalanche detection in ground-based photographs with deep learning J. Fox et al. https://doi.org/10.1016/j.coldregions.2024.104179
- Past and future changes in avalanche problems in northern Norway estimated with machine-learning models K. Eiselt & R. Graversen https://doi.org/10.5194/tc-20-1867-2026
- Application of Artificial Intelligence in the Assessment and Forecast of Avalanche Danger in the Ile Alatau Ridge V. Blagovechshenskiy et al. https://doi.org/10.3390/w15071438
- Automated prediction of wet-snow avalanche activity in the Swiss Alps M. Hendrick et al. https://doi.org/10.1017/jog.2023.24
- The avalanche risk prediction intelligent system: Susa Valley Alps case study H. Bui & S. Distefano https://doi.org/10.1186/s40537-026-01450-4
- Addressing class imbalance in avalanche forecasting M. Kala et al. https://doi.org/10.1016/j.coldregions.2024.104411
- SLABS: An improved probabilistic method to assess the avalanche risk on backcountry ski tours B. Degraeuwe et al. https://doi.org/10.1016/j.coldregions.2024.104169
- Assessing the performance and explainability of an avalanche danger forecast model C. Pérez-Guillén et al. https://doi.org/10.5194/nhess-25-1331-2025
- From the Swiss Alps to the Pyrenees: Evaluating the transferability of machine learning models for avalanche forecasting C. Pérez-Guillén et al. https://doi.org/10.1016/j.coldregions.2026.104884
- Exploring motivations of winter backcountry recreationists in support of avalanche risk communication A. Neweduk & P. Haegeli https://doi.org/10.1016/j.jort.2025.100892
- Spatial heterogeneity and temporal tendency of channeled snow avalanche activity retrieved from Landsat images in the maritime snow climate of the Parlung Tsangpo catchment, southeastern Tibet H. Wen et al. https://doi.org/10.1016/j.coldregions.2024.104206
- Creating probability maps for avalanche hazardous areas reflecting snowpack uncertainty updates T. Tanabe et al. https://doi.org/10.1016/j.coldregions.2025.104716
36 citations as recorded by crossref.
- The EAWS matrix, a decision support tool to determine the regional avalanche danger level (Part A): conceptual development K. Müller et al. https://doi.org/10.5194/nhess-25-4503-2025
- Probability models to convert snowpack stability into the number of dry-snow avalanches in North Japan Y. Katsuyama et al. https://doi.org/10.1016/j.coldregions.2025.104480
- Early warning – international collaboration using map-based and user-centered approaches C. Lienert https://doi.org/10.1080/23729333.2025.2479429
- Changing drivers of regional large magnitude avalanche frequency throughout Colorado, USA E. Peitzsch et al. https://doi.org/10.5194/nhess-26-1059-2026
- Combining modelled snowpack stability with machine learning to predict avalanche activity L. Viallon-Galinier et al. https://doi.org/10.5194/tc-17-2245-2023
- Clustering simulated snow profiles to form avalanche forecast regions S. Horton et al. https://doi.org/10.5194/gmd-18-193-2025
- Development and evaluation of a method to identify potential release areas of snow avalanches based on watershed delineation C. Duvillier et al. https://doi.org/10.5194/nhess-23-1383-2023
- A neural network model for automated prediction of avalanche danger level V. Sharma et al. https://doi.org/10.5194/nhess-23-2523-2023
- Can model-based avalanche forecasts match the discriminatory skill of human danger-level forecasts? A comparison from Switzerland F. Techel et al. https://doi.org/10.5194/nhess-25-3333-2025
- A quantitative module of avalanche hazard – comparing forecaster assessments of storm and persistent slab avalanche problems with information derived from distributed snowpack simulations F. Herla et al. https://doi.org/10.5194/nhess-25-625-2025
- Empirical Study on the Conditions for Blowing Snow Events at Roads on High-Altitude Mountains L. Feng et al. https://doi.org/10.1007/s13351-025-4192-4
- Cooperative avalanche beacon localization using machine-learned geometric constraints and sequential regression-plane intersection T. Yonekura et al. https://doi.org/10.1016/j.coldregions.2026.105005
- A three-stage model pipeline predicting regional avalanche danger in Switzerland (RAvaFcast v1.0.0): a decision-support tool for operational avalanche forecasting A. Maissen et al. https://doi.org/10.5194/gmd-17-7569-2024
- Relating storm-snow avalanche instabilities to data collected from the Differential Emissivity Imaging Disdrometer (DEID) T. Morrison et al. https://doi.org/10.1016/j.coldregions.2023.103839
- Spatially continuous snow depth mapping by aeroplane photogrammetry for annual peak of winter from 2017 to 2021 in open areas L. Bührle et al. https://doi.org/10.5194/tc-17-3383-2023
- Towards deep-learning solutions for classification of automated snow height measurements (CleanSnow v1.0.2) J. Svoboda et al. https://doi.org/10.5194/gmd-18-1829-2025
- Prediction of natural dry-snow avalanche activity using physics-based snowpack simulations S. Mayer et al. https://doi.org/10.5194/nhess-23-3445-2023
- Selection of Significant Features for Snow Avalanche Forecasting K. Kaushik et al. https://doi.org/10.1007/s10666-025-10061-x
- Assessing the predictive capability of several machine learning algorithms to forecast snow avalanches using numerical weather prediction model in eastern Canada F. Gauthier et al. https://doi.org/10.5194/nhess-25-5033-2025
- Acoustic emission monitoring and analyses for avalanche release and slab fracturing events observed in Great Himalaya J. Kapil et al. https://doi.org/10.1080/10589759.2023.2208717
- Predicting avalanche danger in northern Norway using statistical models K. Eiselt & R. Graversen https://doi.org/10.5194/tc-19-1849-2025
- Impact of climate change on snow avalanche activity in the Swiss Alps S. Mayer et al. https://doi.org/10.5194/tc-18-5495-2024
- Climate change impacts on snow avalanche activity and related risks N. Eckert et al. https://doi.org/10.1038/s43017-024-00540-2
- Climate snow-avalanche linkage revealed by geomorphological, historical and tree-ring records in the central Spanish Pyrenees O. Franco-Ramos et al. https://doi.org/10.1016/j.coldregions.2026.104819
- Automating avalanche detection in ground-based photographs with deep learning J. Fox et al. https://doi.org/10.1016/j.coldregions.2024.104179
- Past and future changes in avalanche problems in northern Norway estimated with machine-learning models K. Eiselt & R. Graversen https://doi.org/10.5194/tc-20-1867-2026
- Application of Artificial Intelligence in the Assessment and Forecast of Avalanche Danger in the Ile Alatau Ridge V. Blagovechshenskiy et al. https://doi.org/10.3390/w15071438
- Automated prediction of wet-snow avalanche activity in the Swiss Alps M. Hendrick et al. https://doi.org/10.1017/jog.2023.24
- The avalanche risk prediction intelligent system: Susa Valley Alps case study H. Bui & S. Distefano https://doi.org/10.1186/s40537-026-01450-4
- Addressing class imbalance in avalanche forecasting M. Kala et al. https://doi.org/10.1016/j.coldregions.2024.104411
- SLABS: An improved probabilistic method to assess the avalanche risk on backcountry ski tours B. Degraeuwe et al. https://doi.org/10.1016/j.coldregions.2024.104169
- Assessing the performance and explainability of an avalanche danger forecast model C. Pérez-Guillén et al. https://doi.org/10.5194/nhess-25-1331-2025
- From the Swiss Alps to the Pyrenees: Evaluating the transferability of machine learning models for avalanche forecasting C. Pérez-Guillén et al. https://doi.org/10.1016/j.coldregions.2026.104884
- Exploring motivations of winter backcountry recreationists in support of avalanche risk communication A. Neweduk & P. Haegeli https://doi.org/10.1016/j.jort.2025.100892
- Spatial heterogeneity and temporal tendency of channeled snow avalanche activity retrieved from Landsat images in the maritime snow climate of the Parlung Tsangpo catchment, southeastern Tibet H. Wen et al. https://doi.org/10.1016/j.coldregions.2024.104206
- Creating probability maps for avalanche hazardous areas reflecting snowpack uncertainty updates T. Tanabe et al. https://doi.org/10.1016/j.coldregions.2025.104716
Saved (final revised paper)
Latest update: 25 Aug 2026
Editorial statement
The paper could have a strong impact in the entire Alpine region, where avalanche forecasting is a critical issue to manage
The paper could have a strong impact in the entire Alpine region, where avalanche forecasting is...
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.
A fully data-driven approach to predicting the danger level for dry-snow avalanche conditions in...
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