Preprints
https://doi.org/10.5194/tc-2022-34
https://doi.org/10.5194/tc-2022-34
 
10 Mar 2022
10 Mar 2022
Status: this preprint is currently under review for the journal TC.

A random forest model to assess snow instability from simulated snow stratigraphy

Stephanie Mayer, Alec van Herwijnen, Frank Techel, and Jürg Schweizer Stephanie Mayer et al.
  • WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland

Abstract. Modeled snow stratigraphy and instability data are a promising source of information for avalanche forecasting. While instability indices describing the mechanical processes of dry-snow avalanche release have been implemented into snow cover models, there exists no readily applicable method that combines these metrics to predict snow instability. We therefore trained a random forest (RF) classification model to assess snow instability from snow stratigraphy simulated with SNOWPACK. To do so, we manually compared 742 observed snow profiles with their simulated counterparts to select the simulated weak layer corresponding to the observed rutschblock failure layer. We then used the observed stability test result and an estimate of the local avalanche danger to construct a binary target variable (stable vs. unstable) and considered 34 features describing the simulated weak layer and the overlying slab as potential explanatory variables. The final RF classifier aggregates six of these features into the output probability Punstable, corresponding to the mean vote of an ensemble of 400 classification trees. Although the training data only consisted of 146 manual profiles labeled as either unstable or stable, the model classified profiles from an independent validation data set with high reliability (accuracy: 88 %, precision: 96 %, recall: 85 %) using manually predefined weak layers. Model performance was even higher (accuracy: 93 %, precision: 96 %, recall: 92 %), when the weakest layers of the profiles were identified with the maximum of Punstable. Finally, we compared model predictions to observed avalanche activity in the region of Davos for five winter seasons. In 73 % of the days, our model correctly discriminated between avalanche days and non-avalanche days. Overall, the results of our RF classification are very encouraging, suggesting it could be of great value for operational avalanche forecasting.

Stephanie Mayer et al.

Status: open (until 16 Jun 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-34', Pascal Hagenmuller, 07 Apr 2022 reply

Stephanie Mayer et al.

Stephanie Mayer et al.

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Short summary
Information on snow instability is crucial for avalanche forecasting. We introduce a novel, machine-learning based method to assess snow instability from snow stratigraphy simulated with the snow cover model SNOWPACK. To develop the model, we compared observed and simulated snow profiles. Our model provides a probability of instability for every layer of a simulated snow profile, which allows detecting the weakest layer and assessing its degree of instability with one single index.