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

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 snow profiles observed in the Swiss Alps with their simulated counterparts and selected 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 subset of training data only consisted of 146 profiles labeled as either unstable or stable, the model classified profiles from an independent validation data set (N=121) 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. Of the 252 avalanche days (345 non-avalanche days), 69 % (75 %) were classified correctly. Overall, the results of our RF classification are very encouraging, suggesting it could be of great value for operational avalanche forecasting.