Combining modelled snowpack stability with machine learning to predict avalanche activity

Viallon-Galinier, Léo; Hagenmuller, Pascal; Eckert, Nicolas

doi:https://doi.org/10.5194/tc-17-2245-2023

Articles | Volume 17, issue 6

https://doi.org/10.5194/tc-17-2245-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/tc-17-2245-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 17, issue 6

Research article

|

05 Jun 2023

Research article |

| 05 Jun 2023

Combining modelled snowpack stability with machine learning to predict avalanche activity

Léo Viallon-Galinier, Pascal Hagenmuller, and Nicolas Eckert

Data sets

The S2M meteorological and snow cover reanalysis in the French mountainous areas (1958-present) M. Vernay, M. Lafaysse, P. Hagenmuller, R. Nheili, D. Verfaillie, and S. Morin https://doi.org/10.25326/37

Short summary

Avalanches are a significant issue in mountain areas where they threaten recreationists and human infrastructure. Assessments of avalanche hazards and the related risks are therefore an important challenge for local authorities. Meteorological and snow cover simulations are thus important to support operational forecasting. In this study we combine it with mechanical analysis of snow profiles and find that observed avalanche data improve avalanche activity prediction through statistical methods.