Articles | Volume 25, issue 3
https://doi.org/10.5194/nhess-25-1255-2025
https://doi.org/10.5194/nhess-25-1255-2025
Research article
 | 
02 Apr 2025
Research article |  | 02 Apr 2025

Development of operational decision support tools for mechanized ski guiding using avalanche terrain modeling, GPS tracking, and machine learning

John Sykes, Pascal Haegeli, Roger Atkins, Patrick Mair, and Yves Bühler

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Cited articles

Atkins, R.: Yin, Yang, and You, in: Proceedings International Snow Science Workshop, 29 September–3 October 2014, Banff, AB, Canada, Citation Key: Atkins2014, https://arc.lib.montana.edu/snow-science/item.php?id=2054 (last access: March 2025), 2014. 
Bolognesi, D. R.: Nivotest: A Pocket Tool for Avalanche Risk Assessing, Proceedings of the 2000 International Snow Science Workshop, 1–6 October 2000, Big Sky, Montana, USA, 554–557, https://arc.lib.montana.edu/snow-science/item.php?id=791 (last access: March 2025), 2000. 
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. 
Bühler, Y., Kumar, S., Veitinger, J., Christen, M., Stoffel, A., and Snehmani: Automated identification of potential snow avalanche release areas based on digital elevation models, Nat. Hazards Earth Syst. Sci., 13, 1321–1335, https://doi.org/10.5194/nhess-13-1321-2013, 2013. 
Bühler, Y., von Rickenbach, D., Stoffel, A., Margreth, S., Stoffel, L., and Christen, M.: Automated snow avalanche release area delineation – validation of existing algorithms and proposition of a new object-based approach for large-scale hazard indication mapping, Nat. Hazards Earth Syst. Sci., 18, 3235–3251, https://doi.org/10.5194/nhess-18-3235-2018, 2018. 
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Short summary
We model the decision-making of professional ski guides and develop decision support tools to assist with determining appropriate terrain based on current conditions. Our approach compares a manually constructed Bayesian network with machine learning classification models. The models accurately capture the real-world decision-making outcomes in 85–93 % of cases. Our conclusions focus on strengths and weaknesses of each model and discuss ramifications for practical applications in ski guiding.
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