03 Jun 2022
03 Jun 2022
Status: this preprint is currently under review for the journal TC.

Combining snow physics and machine learning to predict avalanche activity: does it help?

Léo Viallon-Galinier1,2,3, Pascal Hagenmuller1, and Nicolas Eckert2 Léo Viallon-Galinier et al.
  • 1Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’Études de la Neige, Grenoble, France
  • 2Univ. Grenoble Alpes, IRSTEA, UR ETNA, Grenoble, France
  • 3École des Ponts, Champs-sur-Marne, France

Abstract. Predicting avalanche activity from meteorological and snow cover simulations is critical in mountainous areas to support operational forecasting. Several numerical and statistical methods have tried to address this issue. However, it remains unclear how the combination of snow physics, mechanical analysis of snow profiles and observed avalanche data improves avalanche activity prediction. This study combines extensive snow cover and snow stability simulations with observed avalanche occurrences within a Random Forest approach to predict avalanche days at a spatial resolution corresponding to elevations and aspects of avalanche paths in a given mountain range. We develop a rigorous leave-one-out evaluation procedure including an independent test set, confusion matrices, and receiver operating characteristic curves. In a region of the French Alps (Haute-Maurienne) and over the period 1960–2018, we show the added value within the statistical model of considering advanced snow cover modelling and mechanical stability indices instead of using only simple meteorological and bulk information. Specifically, using mechanically-based stability indices and their time-derivatives in addition to simple snow and meteorological variables increases the recall from around 65 % to 76 %. However, due to the scarcity of avalanche events and the possible misclassification of non-avalanche days in the training data set, the precision remains low, around 3.5 %, due to the scarcity of avalanche days. These scores illustrate the difficulty of predicting avalanche occurrence with a high spatio-temporal resolution, even with the current cutting-edge data and modelling tools. Yet, our study opens perspectives to improve modelling tools supporting operational avalanche forecasting.

Léo Viallon-Galinier et al.

Status: open (until 29 Jul 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-108', Frank Techel, 28 Jun 2022 reply

Léo Viallon-Galinier et al.

Léo Viallon-Galinier et al.


Total article views: 287 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
222 55 10 287 2 3
  • HTML: 222
  • PDF: 55
  • XML: 10
  • Total: 287
  • BibTeX: 2
  • EndNote: 3
Views and downloads (calculated since 03 Jun 2022)
Cumulative views and downloads (calculated since 03 Jun 2022)

Viewed (geographical distribution)

Total article views: 273 (including HTML, PDF, and XML) Thereof 273 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 30 Jun 2022
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 observed avalanche data improves avalanche activity prediction through statistical methods.