Preprints
https://doi.org/10.5194/nhess-2021-341
https://doi.org/10.5194/nhess-2021-341

  22 Nov 2021

22 Nov 2021

Review status: this preprint is currently under review for the journal NHESS.

Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland

Cristina Pérez-Guillén1, Frank Techel1, Martin Hendrick1, Michele Volpi2, Alec van Herwijnen1, Tasko Olevski2, Guillaume Obozinski2, Fernando Pérez-Cruz2, and Jürg Schweizer1 Cristina Pérez-Guillén et al.
  • 1WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
  • 2Swiss Data Science Center, Zurich, Switzerland

Abstract. Even today, the assessment of avalanche danger is by large a subjective, yet data-based decision-making process. Human experts analyze heterogeneous data volumes, diverse in scale, and conclude on the avalanche scenario based on their experience. Nowadays, modern machine learning methods and the rise in computing power in combination with physical snow cover modelling open up new possibilities for developing decision support tools for operational avalanche forecasting. Therefore, we developed a fully data-driven approach to predict the regional avalanche danger level, the key component in public avalanche forecasts, for dry-snow conditions in the Swiss Alps. Using a large data set of more than 20 years of meteorological data measured by a network of automated weather stations, which are located at the elevation of potential avalanche starting zones, and snow cover simulations driven with these input weather data, we trained two random forest (RF) classifiers. The first classifier (RF #1) was trained relying on the forecast danger levels published in the avalanche bulletin. Given the uncertainty related to a forecast danger level as a target variable, we trained a second classifier (RF #2), relying on a quality-controlled subset of danger level labels. We optimized the RF classifiers by selecting the best set of input features combining meteorological variables and features extracted from the simulated profiles. The accuracy of the danger level predictions ranged between 74 % and 76 % for RF #1, and between 72 % and 78 % for RF #2, with both models achieving better performance than previously developed methods. To assess the accuracy of the forecast, and thus the quality of our labels, we relied on nowcast assessments of avalanche danger by well-trained observers. The performance of both models was similar to the accuracy of the current experience-based Swiss avalanche forecasts (which is estimated to 76 %). The models performed consistently well throughout the Swiss Alps, thus in different climatic regions, albeit with some regional differences. A prototype model with the RF classifiers was already tested in a semi-operational setting by the Swiss avalanche warning service during the winter 2020-2021. The promising results suggest that the model may well have potential to become a valuable, supplementary decision support tool for avalanche forecasters when assessing avalanche hazard.

Cristina Pérez-Guillén et al.

Status: open (until 03 Jan 2022)

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Cristina Pérez-Guillén et al.

Cristina Pérez-Guillén et al.

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Short summary
We developed a fully data-driven approach to automatically predict the danger level for dry-snow avalanche conditions in Switzerland. Two random forest classifiers were trained using a large database of meteorological data, snow cover simulations, and danger levels from public forecasts. The performance of both models was similar to the accuracy of the Swiss avalanche forecasts. These models have the potential to become a valuable supplementary decision support tool to assess avalanche hazard.
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