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Abstract

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 that can occur due to time pressure and emotional investment. While guiding teams pass on this expertise through mentorship, the current lack of a quantitative description of the process prevents the development of decision aids. To address this knowledge gap, we collaborated with guides at Canadian Mountain Holidays (CMH) Galena Lodge to catalog and analyze their decision-making process using operational data, GPS tracking, automated avalanche hazard indication mapping, and Bayesian networks. Our initial results use Bayesian networks to model the daily run list decision-making process, where guides code runs as either black (not considered), red (closed), or green (open). To capture the real world decision-making process, we worked with expert guides to build the structure of the decision-making network. Data relevant to run characteristics, current conditions, and prior run list decisions are used to calculate the conditional probability tables of the decision-making model. Our resulting model illustrates the decision-making process and can predict the run list coding with accuracy of roughly 87% compared to 80,854 observed run codes from CMH Galena. These insights provide a baseline for development of future decision-making tools that can offer independent perspectives on operational terrain choices based on historic patterns or as a training tool for newer guides.