Estimating the danger of snow avalanches with a machine learning approach using a comprehensive snow cover model

The avalanche warning services use the regional avalanche danger level and activity as key metrics. In this study a dataset of a ski resort from 2012 to 2020 was used. The local avalanche danger level, the success of artificial avalanche release by snow groomer or blasting, and the decision to try an avalanche release by blasting are target variables. Five meteorological recordings and twenty five variables of the modeled snowpack with an hourly resolution were used as input for the machine learning approach. An artificial neural network consists of recurrent layers and convolutional layers for merging the temporal and spatial data. Support vector machines were adapted to calculate the probabilities for the target variables. A logistic regression was used as ensemble method as stacked generalization. The accuracy of the models, i.e., the precision for predicting the local avalanche danger level was 0.73 and 0.52 for the train and test data, respectively. The precisions for artificial avalanche release by snow groomer were 0.94 and 0.85, for artificial avalanche release by blasting 0.75 and 0.72, and for the decision to try an avalanche release by blasting 0.75 and 0.70. Unlike previous studies, which used regional avalanche danger levels as target variable, we used local warning levels and additional target variables. The comparison shows that despite differences similar accuracy can be achieved. We further identified opportunities to optimize the model by examining the relevance of the input variables and performing a sensitivity analysis.