Development of operational decision support tools for mechanized ski guiding using avalanche terrain modeling, GPS tracking, and machine learning
<p>Snow avalanches are the primary mountain hazard for mechanized skiing operations. Helicopter and snowcat ski guides are tasked with finding safe terrain to provide guests with enjoyable skiing in a fast-paced and highly dynamic and complex decision environment. Based on years of experience,...
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Copernicus Publications
2025-04-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | https://nhess.copernicus.org/articles/25/1255/2025/nhess-25-1255-2025.pdf |
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| author | J. Sykes J. Sykes P. Haegeli R. Atkins P. Mair Y. Bühler Y. Bühler |
| author_facet | J. Sykes J. Sykes P. Haegeli R. Atkins P. Mair Y. Bühler Y. Bühler |
| author_sort | J. Sykes |
| collection | DOAJ |
| description | <p>Snow avalanches are the primary mountain hazard for mechanized skiing operations. Helicopter and snowcat ski guides are tasked with finding safe terrain to provide guests with enjoyable skiing in a fast-paced and highly dynamic and complex decision environment. Based on years of experience, ski guides have established systematic decision-making practices that streamline the process and limit the potential negative influences of time pressure and emotional investment. While this expertise is shared within guiding teams through mentorship, the current lack of a quantitative description of the process prevents the development of decision aids that could strengthen the process. To address this knowledge gap, we collaborated with guides at Canadian Mountain Holidays (CMH) Galena Lodge to catalogue and analyze their decision-making process for the daily run list, where they code runs as green (open for guiding), red (closed), or black (not considered) before heading into the field. To capture the real-world decision-making process, we first built the structure of the decision-making process with input from guides and then used a wide range of available relevant data indicative of run characteristics, current conditions, and prior run list decisions to create the features of the models. We employed three different modeling approaches to capture the run list decision-making process: Bayesian network, random forest, and extreme gradient boosting. The overall accuracies of the models are 84.6 %, 91.9 %, and 93.3 % respectively compared to a testing dataset of roughly 20 000 observed run codes. The insights of our analysis provide a baseline for the development of effective decision support tools for backcountry avalanche risk management that can offer independent perspectives on operational terrain choices based on historic patterns or as a training tool for newer guides.</p> |
| format | Article |
| id | doaj-art-51f62b5165984dc3a46cac3d1fce90bf |
| institution | OA Journals |
| issn | 1561-8633 1684-9981 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Natural Hazards and Earth System Sciences |
| spelling | doaj-art-51f62b5165984dc3a46cac3d1fce90bf2025-08-20T01:55:12ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812025-04-01251255129210.5194/nhess-25-1255-2025Development of operational decision support tools for mechanized ski guiding using avalanche terrain modeling, GPS tracking, and machine learningJ. Sykes0J. Sykes1P. Haegeli2R. Atkins3P. Mair4Y. Bühler5Y. Bühler6Department of Geography, Simon Fraser University, Burnaby, BC, CanadaChugach National Forest Avalanche Center, Girdwood, AK, USASchool of Resource and Environmental Management, Simon Fraser University, Burnaby, BC, CanadaCanadian Mountain Holidays, Banff, AB, CanadaDepartment of Psychology, Harvard University, Cambridge, MA, USAWSL Institute for Snow and Avalanche Research SLF, Davos, SwitzerlandClimate Change, Extremes and Natural Hazards in Alpine Regions Research Centre CERC, Davos, Switzerland<p>Snow avalanches are the primary mountain hazard for mechanized skiing operations. Helicopter and snowcat ski guides are tasked with finding safe terrain to provide guests with enjoyable skiing in a fast-paced and highly dynamic and complex decision environment. Based on years of experience, ski guides have established systematic decision-making practices that streamline the process and limit the potential negative influences of time pressure and emotional investment. While this expertise is shared within guiding teams through mentorship, the current lack of a quantitative description of the process prevents the development of decision aids that could strengthen the process. To address this knowledge gap, we collaborated with guides at Canadian Mountain Holidays (CMH) Galena Lodge to catalogue and analyze their decision-making process for the daily run list, where they code runs as green (open for guiding), red (closed), or black (not considered) before heading into the field. To capture the real-world decision-making process, we first built the structure of the decision-making process with input from guides and then used a wide range of available relevant data indicative of run characteristics, current conditions, and prior run list decisions to create the features of the models. We employed three different modeling approaches to capture the run list decision-making process: Bayesian network, random forest, and extreme gradient boosting. The overall accuracies of the models are 84.6 %, 91.9 %, and 93.3 % respectively compared to a testing dataset of roughly 20 000 observed run codes. The insights of our analysis provide a baseline for the development of effective decision support tools for backcountry avalanche risk management that can offer independent perspectives on operational terrain choices based on historic patterns or as a training tool for newer guides.</p>https://nhess.copernicus.org/articles/25/1255/2025/nhess-25-1255-2025.pdf |
| spellingShingle | J. Sykes J. Sykes P. Haegeli R. Atkins P. Mair Y. Bühler Y. Bühler Development of operational decision support tools for mechanized ski guiding using avalanche terrain modeling, GPS tracking, and machine learning Natural Hazards and Earth System Sciences |
| title | Development of operational decision support tools for mechanized ski guiding using avalanche terrain modeling, GPS tracking, and machine learning |
| title_full | Development of operational decision support tools for mechanized ski guiding using avalanche terrain modeling, GPS tracking, and machine learning |
| title_fullStr | Development of operational decision support tools for mechanized ski guiding using avalanche terrain modeling, GPS tracking, and machine learning |
| title_full_unstemmed | Development of operational decision support tools for mechanized ski guiding using avalanche terrain modeling, GPS tracking, and machine learning |
| title_short | Development of operational decision support tools for mechanized ski guiding using avalanche terrain modeling, GPS tracking, and machine learning |
| title_sort | development of operational decision support tools for mechanized ski guiding using avalanche terrain modeling gps tracking and machine learning |
| url | https://nhess.copernicus.org/articles/25/1255/2025/nhess-25-1255-2025.pdf |
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