Articles | Volume 25, issue 11
https://doi.org/10.5194/nhess-25-4375-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-4375-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Nov 2025
Research article |
| 07 Nov 2025
| 07 Nov 2025
Machine learning for automated avalanche terrain exposure scale (ATES) classification
National Institute of Geophysics, Geodesy and Geography, Bulgarian Academy of Sciences, Sofia, Bulgaria
Bulgarian Avalanche Association, Sofia, Bulgaria
Andreas Huber
Austrian Research Centre for Forests (BFW), Department of Natural Hazards, Innsbruck, Tyrol, Austria
University of Innsbruck, Unit of Hydraulic Engineering, Innsbruck, Tyrol, Austria
Momchil Panayotov
University of Forestry, Sofia, Bulgaria
Bulgarian Extreme and FreeSkiing Association (BEFSA), Sofia, Bulgaria
Bulgarian Avalanche Association, Sofia, Bulgaria
Christoph Hesselbach
Austrian Research Centre for Forests (BFW), Department of Natural Hazards, Innsbruck, Tyrol, Austria
Paula Spannring
Austrian Research Centre for Forests (BFW), Department of Natural Hazards, Innsbruck, Tyrol, Austria
Jan-Thomas Fischer
Austrian Research Centre for Forests (BFW), Department of Natural Hazards, Innsbruck, Tyrol, Austria
Michaela Teich
Austrian Research Centre for Forests (BFW), Department of Natural Hazards, Innsbruck, Tyrol, Austria
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Michael Neuhauser, Anselm Köhler, Anna Wirbel, Felix Oesterle, Wolfgang Fellin, Johannes Gerstmayr, Falko Dressler, and Jan-Thomas Fischer
Nat. Hazards Earth Syst. Sci., 25, 4185–4202, https://doi.org/10.5194/nhess-25-4185-2025, https://doi.org/10.5194/nhess-25-4185-2025, 2025
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This study examines how particles move in snow avalanches. The researchers used AvaNodes, a sensor system that tracks particle movement, in combination with radar data and simulations from the open avalanche framework AvaFrame. By comparing measurements and simulations, particle velocity and avalanche front position were matched with high accuracy. The study illustrates how multiple parameter sets can yield appropriate results and highlights the complexity of avalanche simulation.
Matthias Tonnel, Anna Wirbel, Felix Oesterle, and Jan-Thomas Fischer
Geosci. Model Dev., 16, 7013–7035, https://doi.org/10.5194/gmd-16-7013-2023, https://doi.org/10.5194/gmd-16-7013-2023, 2023
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Avaframe - the open avalanche framework - provides open-source tools to simulate and investigate snow avalanches. It is utilized for multiple purposes, the two main applications being hazard mapping and scientific research of snow processes. We present the theory, conversion to a computer model, and testing for one of the core modules used for simulations of a particular type of avalanche, the so-called dense-flow avalanches. Tests check and confirm the applicability of the utilized method.
Christopher J. L. D'Amboise, Michael Neuhauser, Michaela Teich, Andreas Huber, Andreas Kofler, Frank Perzl, Reinhard Fromm, Karl Kleemayr, and Jan-Thomas Fischer
Geosci. Model Dev., 15, 2423–2439, https://doi.org/10.5194/gmd-15-2423-2022, https://doi.org/10.5194/gmd-15-2423-2022, 2022
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The term gravitational mass flow (GMF) covers various natural hazard processes such as snow avalanches, rockfall, landslides, and debris flows. Here we present the open-source GMF simulation tool Flow-Py. The model equations are based on simple geometrical relations in three-dimensional terrain. We show that Flow-Py is an educational, innovative GMF simulation tool with three computational experiments: 1. validation of implementation, 2. performance, and 3. expandability.
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Short summary
With growing demand for decision support in recreational and professional use of avalanche terrain, we applied machine learning for automated Avalanche Terrain Exposure Scale (AutoATES) mapping in Bulgaria. A Random Forest model, trained on expert-labelled data from the Pirin Mountains, accurately classifies avalanche terrain and reduces reliance on manual expert mapping, offering an effective and scalable solution for large-scale regional AutoATES applications.
With growing demand for decision support in recreational and professional use of avalanche...
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