Use Case Description
Ski touring requires physical fitness, good equipment, technical skills and experience in assessing danger or difficulty in the terrain. Accidents are frequent. Some of them avoidable. www.skitourenguru.ch is a free of cost web service that publishes twice a day avalanche risk assessment and difficulty levels for backcountry ski tours in Switzerland. Its purpose is the reduction of avalanche and climbing accidents by means of adequate tour selection. The success of this service in Switzerland created demand in neighboring countries for Austria, Italy and France. An international extension is currently under construction. The Hackathlon use case focuses on the extension of tour difficulties to other countries.
The published technical tour difficulty level from the literature of the Swiss Alpine Club (SAC) is an important criterion for route selection. To extend this SAC metric to the entire Alpine region, the question arises if a machine learning method can determine tour difficulties consistent with the published SAC difficulties for the Swiss Alps and be applied to score tours in neighboring countries. Ideally such automatic method should provide full transparency about what determines the difficulty level of each ski tour (white box algorithm). Skitourenguru gathered the training data to explain the SAC difficulty levels of 1307 ski tours in Switzerland and scoring data from tours in neighboring countries. Each tour is decomposed into 10m segments, and their local topographic information such as slope, fall speed, forestation, curvature, etc. assigned from a digital elevation and landscape model. Methods of machine learning, variable selection, linear optimization, in combination with statistical techniques might provide an answer to predict technical difficulty levels. The final model should extend the SAC difficulty metric to the entire Alpine area, but also localize and visualize the partial difficulties along each route on the map.
Expected advantages are:
1) consistent ratings without author or regional bias, 2)
interpretability of difficulty ratings down to single segments of a tour, 3)
efficient initial- and reevaluation of large tour datasets
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Team Name
SiberianSnowTigers
Track
Data for Good
Use Case
Predictions of Ski Tour Difficulties
Technology
Data Science, Machine Learning, Statistics, Data Management
Region
EMEA
Team lead
Alice
Team members
Alice, Günter
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