Articles | Volume 25, issue 3
https://doi.org/10.5194/nhess-25-1255-2025
© 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-25-1255-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Apr 2025
Research article |
| 02 Apr 2025
| 02 Apr 2025
Development of operational decision support tools for mechanized ski guiding using avalanche terrain modeling, GPS tracking, and machine learning
Department of Geography, Simon Fraser University, Burnaby, BC, Canada
Chugach National Forest Avalanche Center, Girdwood, AK, USA
Pascal Haegeli
School of Resource and Environmental Management, Simon Fraser University, Burnaby, BC, Canada
Roger Atkins
Canadian Mountain Holidays, Banff, AB, Canada
Patrick Mair
Department of Psychology, Harvard University, Cambridge, MA, USA
Yves Bühler
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Climate Change, Extremes and Natural Hazards in Alpine Regions Research Centre CERC, Davos, Switzerland
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Aubrey Miller, Pascal Sirguey, Simon Morris, Perry Bartelt, Nicolas Cullen, Todd Redpath, Kevin Thompson, and Yves Bühler
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Florian Herla, Pascal Haegeli, and Patrick Mair
The Cryosphere, 16, 3149–3162, https://doi.org/10.5194/tc-16-3149-2022, https://doi.org/10.5194/tc-16-3149-2022, 2022
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We present an averaging algorithm for multidimensional snow stratigraphy profiles that elicits the predominant snow layering among large numbers of profiles and allows for compiling of informative summary statistics and distributions of snowpack layer properties. This creates new opportunities for presenting and analyzing operational snowpack simulations in support of avalanche forecasting and may inspire new ways of processing profiles and time series in other geophysical contexts.
Adrian Ringenbach, Elia Stihl, Yves Bühler, Peter Bebi, Perry Bartelt, Andreas Rigling, Marc Christen, Guang Lu, Andreas Stoffel, Martin Kistler, Sandro Degonda, Kevin Simmler, Daniel Mader, and Andrin Caviezel
Nat. Hazards Earth Syst. Sci., 22, 2433–2443, https://doi.org/10.5194/nhess-22-2433-2022, https://doi.org/10.5194/nhess-22-2433-2022, 2022
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Forests have a recognized braking effect on rockfalls. The impact of lying deadwood, however, is mainly neglected. We conducted 1 : 1-scale rockfall experiments in three different states of a spruce forest to fill this knowledge gap: the original forest, the forest including lying deadwood and the cleared area. The deposition points clearly show that deadwood has a protective effect. We reproduced those experimental results numerically, considering three-dimensional cones to be deadwood.
Kathryn C. Fisher, Pascal Haegeli, and Patrick Mair
Nat. Hazards Earth Syst. Sci., 22, 1973–2000, https://doi.org/10.5194/nhess-22-1973-2022, https://doi.org/10.5194/nhess-22-1973-2022, 2022
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Avalanche bulletins include travel and terrain statements to provide recreationists with tangible guidance about how to apply the hazard information. We examined which bulletin users pay attention to these statements, what determines their usefulness, and how they could be improved. Our study shows that reducing jargon and adding simple explanations can significantly improve the usefulness of the statements for users with lower levels of avalanche awareness education who depend on this advice.
Yves Bühler, Peter Bebi, Marc Christen, Stefan Margreth, Lukas Stoffel, Andreas Stoffel, Christoph Marty, Gregor Schmucki, Andrin Caviezel, Roderick Kühne, Stephan Wohlwend, and Perry Bartelt
Nat. Hazards Earth Syst. Sci., 22, 1825–1843, https://doi.org/10.5194/nhess-22-1825-2022, https://doi.org/10.5194/nhess-22-1825-2022, 2022
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To calculate and visualize the potential avalanche hazard, we develop a method that automatically and efficiently pinpoints avalanche starting zones and simulate their runout for the entire canton of Grisons. The maps produced in this way highlight areas that could be endangered by avalanches and are extremely useful in multiple applications for the cantonal authorities, including the planning of new infrastructure, making alpine regions more safe.
Animesh K. Gain, Yves Bühler, Pascal Haegeli, Daniela Molinari, Mario Parise, David J. Peres, Joaquim G. Pinto, Kai Schröter, Ricardo M. Trigo, María Carmen Llasat, and Heidi Kreibich
Nat. Hazards Earth Syst. Sci., 22, 985–993, https://doi.org/10.5194/nhess-22-985-2022, https://doi.org/10.5194/nhess-22-985-2022, 2022
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To mark the 20th anniversary of Natural Hazards and Earth System Sciences (NHESS), an interdisciplinary and international journal dedicated to the public discussion and open-access publication of high-quality studies and original research on natural hazards and their consequences, we highlight 11 key publications covering major subject areas of NHESS that stood out within the past 20 years.
Natalie Brožová, Tommaso Baggio, Vincenzo D'Agostino, Yves Bühler, and Peter Bebi
Nat. Hazards Earth Syst. Sci., 21, 3539–3562, https://doi.org/10.5194/nhess-21-3539-2021, https://doi.org/10.5194/nhess-21-3539-2021, 2021
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Surface roughness plays a great role in natural hazard processes but is not always well implemented in natural hazard modelling. The results of our study show how surface roughness can be useful in representing vegetation and ground structures, which are currently underrated. By including surface roughness in natural hazard modelling, we could better illustrate the processes and thus improve hazard mapping, which is crucial for infrastructure and settlement planning in mountainous areas.
Kathryn C. Fisher, Pascal Haegeli, and Patrick Mair
Nat. Hazards Earth Syst. Sci., 21, 3219–3242, https://doi.org/10.5194/nhess-21-3219-2021, https://doi.org/10.5194/nhess-21-3219-2021, 2021
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Avalanche warning services publish condition reports to help backcountry recreationists make informed decisions about when and where to travel in avalanche terrain. We tested how different graphic representations of terrain information can affect users’ ability to interpret and apply the provided information. Our study shows that a combined presentation of aspect and elevation information is the most effective. These results can be used to improve avalanche risk communication products.
Nora Helbig, Michael Schirmer, Jan Magnusson, Flavia Mäder, Alec van Herwijnen, Louis Quéno, Yves Bühler, Jeff S. Deems, and Simon Gascoin
The Cryosphere, 15, 4607–4624, https://doi.org/10.5194/tc-15-4607-2021, https://doi.org/10.5194/tc-15-4607-2021, 2021
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The snow cover spatial variability in mountains changes considerably over the course of a snow season. In applications such as weather, climate and hydrological predictions the fractional snow-covered area is therefore an essential parameter characterizing how much of the ground surface in a grid cell is currently covered by snow. We present a seasonal algorithm and a spatiotemporal evaluation suggesting that the algorithm can be applied in other geographic regions by any snow model application.
Pascal Haegeli, Bret Shandro, and Patrick Mair
The Cryosphere, 15, 1567–1586, https://doi.org/10.5194/tc-15-1567-2021, https://doi.org/10.5194/tc-15-1567-2021, 2021
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Numerous large-scale atmosphere–ocean oscillations including the El Niño–Southern Oscillation, the Pacific Decadal Oscillation, the Pacific North American Teleconnection Pattern, and the Arctic Oscillation are known to substantially affect winter weather patterns in western Canada. Using avalanche problem information from public avalanche bulletins, this study presents a new approach for examining the effect of these atmospheric oscillations on the nature of avalanche hazard in western Canada.
Elisabeth D. Hafner, Frank Techel, Silvan Leinss, and Yves Bühler
The Cryosphere, 15, 983–1004, https://doi.org/10.5194/tc-15-983-2021, https://doi.org/10.5194/tc-15-983-2021, 2021
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Satellites prove to be very valuable for documentation of large-scale avalanche periods. To test reliability and completeness, which has not been satisfactorily verified before, we attempt a full validation of avalanches mapped from two optical sensors and one radar sensor. Our results demonstrate the reliability of high-spatial-resolution optical data for avalanche mapping, the suitability of radar for mapping of larger avalanches and the unsuitability of medium-spatial-resolution optical data.
Nora Helbig, Yves Bühler, Lucie Eberhard, César Deschamps-Berger, Simon Gascoin, Marie Dumont, Jesus Revuelto, Jeff S. Deems, and Tobias Jonas
The Cryosphere, 15, 615–632, https://doi.org/10.5194/tc-15-615-2021, https://doi.org/10.5194/tc-15-615-2021, 2021
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The spatial variability in snow depth in mountains is driven by interactions between topography, wind, precipitation and radiation. In applications such as weather, climate and hydrological predictions, this is accounted for by the fractional snow-covered area describing the fraction of the ground surface covered by snow. We developed a new description for model grid cell sizes larger than 200 m. An evaluation suggests that the description performs similarly well in most geographical regions.
Florian Herla, Simon Horton, Patrick Mair, and Pascal Haegeli
Geosci. Model Dev., 14, 239–258, https://doi.org/10.5194/gmd-14-239-2021, https://doi.org/10.5194/gmd-14-239-2021, 2021
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The adoption of snowpack models in support of avalanche forecasting has been limited. To promote their operational application, we present a numerical method for processing multivariate snow stratigraphy profiles of mixed data types. Our algorithm enables applications like dynamical grouping and summarizing of model simulations, model evaluation, and data assimilation. By emulating the human analysis process, our approach will allow forecasters to familiarly interact with snowpack simulations.
Lucie A. Eberhard, Pascal Sirguey, Aubrey Miller, Mauro Marty, Konrad Schindler, Andreas Stoffel, and Yves Bühler
The Cryosphere, 15, 69–94, https://doi.org/10.5194/tc-15-69-2021, https://doi.org/10.5194/tc-15-69-2021, 2021
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In spring 2018 in the alpine Dischma valley (Switzerland), we tested different industrial photogrammetric platforms for snow depth mapping. These platforms were high-resolution satellites, an airplane, unmanned aerial systems and a terrestrial system. Therefore, this study gives a general overview of the accuracy and precision of the different photogrammetric platforms available in space and on earth and their use for snow depth mapping.
Simon Horton, Moses Towell, and Pascal Haegeli
Nat. Hazards Earth Syst. Sci., 20, 3551–3576, https://doi.org/10.5194/nhess-20-3551-2020, https://doi.org/10.5194/nhess-20-3551-2020, 2020
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We investigate patterns in how avalanche forecasters characterize snow avalanche hazard with avalanche problem types. Decision tree analysis was used to investigate both physical influences based on weather and on snowpack variables and operational practices. The results highlight challenges with developing decision aids based on previous hazard assessments.
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.
Bühler, Y., Bebi, P., Christen, M., Margreth, S., Stoffel, L., Stoffel, A., Marty, C., Schmucki, G., Caviezel, A., Kühne, R., Wohlwend, S., and Bartelt, P.: Automated avalanche hazard indication mapping on a statewide scale, Nat. Hazards Earth Syst. Sci., 22, 1825–1843, https://doi.org/10.5194/nhess-22-1825-2022, 2022.
Chen, T. and Guestrin, C.: XGBoost: A Scalable Tree Boosting System, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13–17 August 2016, San Francisco, California, USA, 785–794, https://doi.org/10.1145/2939672.2939785, 2016.
Christen, M., Kowalski, J., and Bartelt, P.: RAMMS: Numerical simulation of dense snow avalanches in three-dimensional terrain, Cold Reg. Sci. Technol., 63, 1–14, https://doi.org/10.1016/j.coldregions.2010.04.005, 2010.
Colorado Avalanche Information Center: US Accidents Table, https://avalanche.state.co.us/accidents/us, last access: 2 July 2024.
Engler, M. and Mersch, J.: SnowCard: Lawinen-Risiko-Check; Risikomanagement für Skitourengeher, Snowboarder, Variantenfahrer, Schneeschuwanderer, Bergverlag Rother, ISBN 978-3937530185, 2001.
Fenton, N. and Neil, M.: Risk Assessment and Decision Analysis with Bayesian Networks, CRC Press, Boca Raton, https://doi.org/10.1201/9780367803018, 2019.
Flora, M. L., Potvin, C. K., McGovern, A., and Handler, S.: A Machine Learning Explainability Tutorial for Atmospheric Sciences, Artificial Intelligence for the Earth Systems, 3, e230018, https://doi.org/10.1175/AIES-D-23-0018.1, 2024.
Haegeli, P.: Avaluator V2.0 – Avalanche accident prevention card, Canadian Avalanche Centre, Revelstoke, BC, Canada, https://avalanche.ca/pages/avaluator (last access: March 2025), 2010.
Haegeli, P. and McCammon, I.: Avaluator – avalanche accident prevention card, Canadian Avalanche Centre, Revelstoke, BC, Canada, 2007.
Haegeli, P. and McClung, D. M.: Expanding the snow-climate classification with avalanche-relevant information: initial description of avalanche winter regimes for southwestern Canada, J. Glaciol., 53, 266–276, https://doi.org/10.3189/172756507782202801, 2007.
Hand, D. J. and Till, R. J.: A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems, Mach. Learn., 45, 171–186, https://doi.org/10.1023/A:1010920819831, 2001.
HeliCat Canada: HeliCat Canada Accreditation Standards, https://www.helicat.org/ (last access: June 2024), 2024.
Højsgaard, S.: Graphical Independence Networks with the gRain Package for R, J. Stat. Softw., 46, 1–26, https://doi.org/10.18637/jss.v046.i10, 2012.
Israelson, C.: A Suggested Conceptual Model for Daily Terrain Use Decisions at Northern Escape Heli-Skiing, The Avalanche Journal, 110, 39–42, https://avalanchejournal.ca/archives/ (last access: March 2025), 2015.
Jamieson, B., Haegeli, P., and Gauthier, D.: Avalanche accidents in Canada, Canadian Avalanche Association, ISBN 978-0-9866597-4-4, 2010.
Kaplan, J.: fastDummies: Fast creation of dummy (binary) columns and rows from categorical variables (Version 1.35) [R package], https://CRAN.R-project.org/package=fastDummies (last access: June 2024), 2020.
Kaufman, L. and Rousseeuw, P. J.: Finding groups in data: an introduction to cluster analysis, Wiley, Hoboken, NJ, USA, 342 pp., ISBN 978-0-471-73578-6, 2005.
Kuhn, M.: Building Predictive Models in R Using the caret Package, J. Stat. Softw., 28, 1–26, https://doi.org/10.18637/jss.v028.i05, 2008.
Kuhn, M. and Johnson, K.: Applied predictive modeling, Springer, 1–600, https://doi.org/10.1007/978-1-4614-6849-3, 2013.
Larcher, M.: Stop or go: Entscheidungsstrategie für Tourengeher, Berg & Steigen, 99, 18–23, 1999.
Liaw, A. and Wiener, M.: Classification and Regression by randomForest, R News, 2, 18–22, 2002.
Lundberg, S. M. and Lee, S.-I.: A Unified Approach to Interpreting Model Predictions, in: Advances in Neural Information Processing Systems, edited by: Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., NeurIPS Foundation, 4765–4774, https://papers.nips.cc/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf (last access: June 2024), 2017.
Maechler, M.: diptest: Hartigan's Dip Test Statistic for Unimodality – Corrected (0.76-0), CRAN, https://doi.org/10.32614/CRAN.package.diptest, 2021.
Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., and Hornik, K.: cluster: Cluster analysis basics and extensions [R package], CRAN, https://CRAN.R-project.org/package=cluster (last access: May 2024), 2022.
Mayer, S., van Herwijnen, A., Techel, F., and Schweizer, J.: A random forest model to assess snow instability from simulated snow stratigraphy, The Cryosphere, 16, 4593–4615, https://doi.org/10.5194/tc-16-4593-2022, 2022.
Molnar, C.: Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, 2nd edn., self-published, https://christophm.github.io/interpretable-ml-book/ (last access: June 2024), 2022.
Munter, W.: Neue Lawinenkunde: Ein Leitfaden fuer die Praxis, in: Verlag des SAC, Bern, Switzerland, ISBN 3-85902-168-0, 1997.
Olaya, V.: Chapter 6 Basic Land-Surface Parameters, in: Developments in Soil Science, vol. 33, Elsevier Science & Technology, 141–169, https://doi.org/10.1016/S0166-2481(08)00006-8, 2009.
R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (last access: July 2024), 2024.
Richter, B., Bavay, M., Hendrick, M., Mayer, S., Mott, R., Guillén, C. P., and van Herwijnen, A.: Towards A Fully Model-Based Avalanche Hazard Assessment: Investigating The Impact Of Input Data On Machine Learning Predictions, International Snow Science Workshop Proceedings 2023, 8–13 October 2023, Bend, Oregon, USA, p. 135, https://arc.lib.montana.edu/snow-science/item.php?id=2865 (last access: March 2025), 2023.
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., and Müller, M.: pROC: an open-source package for R and S+ to analyze and compare ROC curves, BMC Bioinformatics, 12, 77, https://doi.org/10.1186/1471-2105-12-77, 2011.
Schweizer, J. and Lütschg, M.: Characteristics of human-triggered avalanches, Cold Reg. Sci. Technol., 33, 147–162, 2001.
Scutari, M.: Learning Bayesian Networks with the bnlearn R Package, J. Stat. Softw., 35, 1–22, https://doi.org/10.18637/jss.v035.i03, 2010.
Scutari, M. and Denis, J.-B.: Bayesian Networks With Examples in R, CRC Press, ISBN 978-0-367-36651-3, 2021.
Statham, G., Haegeli, P., Birkeland, K. W., Greene, E., Israelson, C., Tremper, B., Stethem, C., Mcmahon, B., White, B., and Kelly, J.: The North American Public Avalanche Danger Scale, in: International Snow Science Workshop Proceedings 2010, 17–22 October 2010, Lake Tahoe, CA, USA, Citation Key: Statham2010, 465–469, https://arc.lib.montana.edu/snow-science/item.php?id=353 (last access: March 2025), 2010.
Statham, G., Haegeli, P., Greene, E., Birkeland, K., Israelson, C., Tremper, B., Stethem, C., McMahon, B., White, B., and Kelly, J.: A conceptual model of avalanche hazard, Nat. Hazards, 90, 663–691, https://doi.org/10.1007/s11069-017-3070-5, 2018.
Sterchi, R. and Haegeli, P.: A method of deriving operation-specific ski run classes for avalanche risk management decisions in mechanized skiing, Nat. Hazards Earth Syst. Sci., 19, 269–285, https://doi.org/10.5194/nhess-19-269-2019, 2019.
Sterchi, R., Haegeli, P., and Mair, P.: Exploring the relationship between avalanche hazard and run list terrain choices at a helicopter skiing operation, Nat. Hazards Earth Syst. Sci., 19, 2011–2026, https://doi.org/10.5194/nhess-19-2011-2019, 2019.
Sykes, J., Haegeli, P., and Bühler, Y.: Automated snow avalanche release area delineation in data-sparse, remote, and forested regions, Nat. Hazards Earth Syst. Sci., 22, 3247–3270, https://doi.org/10.5194/nhess-22-3247-2022, 2022.
Sykes, J., Toft, H., Haegeli, P., and Statham, G.: Automated Avalanche Terrain Exposure Scale (ATES) mapping – local validation and optimization in western Canada, Nat. Hazards Earth Syst. Sci., 24, 947–971, https://doi.org/10.5194/nhess-24-947-2024, 2024a.
Sykes, J., Haegeli, P., and Mair, P.: Development of operational decision support tools for mechanized ski guiding using avalanche terrain modelling, GPS tracking, and machine learning – Code and Data, OSF [data set and code], https://doi.org/10.17605/OSF.IO/6DHMX, 2024b.
Techel, F., Jarry, F., Kronthaler, G., Mitterer, S., Nairz, P., Pavšek, M., Valt, M., and Darms, G.: Avalanche fatalities in the European Alps: long-term trends and statistics, Geogr. Helv., 71, 147–159, https://doi.org/10.5194/gh-71-147-2016, 2016.
Thumlert, S. and Haegeli, P.: Describing the severity of avalanche terrain numerically using the observed terrain selection practices of professional guides, Nat. Hazards, 91, 89–115, https://doi.org/10.1007/s11069-017-3113-y, 2018.
Toft, H. B., Sykes, J., Schauer, A., Hendrikx, J., and Hetland, A.: AutoATES v2.0: Automated Avalanche Terrain Exposure Scale mapping, Nat. Hazards Earth Syst. Sci., 24, 1779–1793, https://doi.org/10.5194/nhess-24-1779-2024, 2024.
Wakefield, B.: Quantitative examination of terrain perception and its effect on ski run choices in expert heli-ski guides, Master's thesis, Simon Fraser University Library, SFU identifier: etd20461, https://summit.sfu.ca/item/19432 (last access: March 2025), 2019.
Walcher, M., Haegeli, P., and Fuchs, S.: Risk of Death and Major Injury from Natural Winter Hazards in Helicopter and Snowcat Skiing in Canada, Wild. Environ. Med., 30, 251–259, https://doi.org/10.1016/j.wem.2019.04.007, 2019.
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.
We model the decision-making of professional ski guides and develop decision support tools to...
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