Combining Artificial Intelligence with Human Expertise to Predict Avalanche Danger Levels

Introduction

Each day Avalanche warden Frank Techel and his team use data to predict the avalanche warning levels for different regions of the Swiss Alps.  This year, they have a new team member - an machine learning model now helps them decide the danger levels for each region.  Sometimes the model’s predictions are spot on. "Sometimes they're clearly not, but even we're wrong sometimes," says Techel. "The good thing is that the models make different mistakes to us”.

The Evolution of Avalanche Prediction

Traditionally, avalanche forecasts have relied heavily on human expertise, particularly in interpreting data from models like SNOWPACK, created by the Swiss Federal Institute for Snow and Avalanche Research (SLF). The SNOWPACK model simulates the layering and evolution of snow cover based on meteorological data, helping forecasters predict the stability of snow layers and assess avalanche risks. In 2019, under the initiative of SLF Director Jürg Schweizer, the SLF introduced a machine learning (ML) model to enhance this process. The AI model independently analyses these simulations and provides its own assessments. This initiative has shown promising results, with the AI often aligning closely with human predictions. However, it's important to note that both humans and machines can err, and their errors typically differ, offering a complementary check on each other's work.

Read more about SNOWPACK in the Cow-Shed article Modelling Snow in Ski Resorts with SNOWPACK.

AI Predictions

Developing this AI model involved extensive data processing and training. Cristina Pérez, a physicist at SLF, utilised 20 years of weather and snow cover data to train the model. One significant challenge was achieving accuracy for high avalanche warning levels due to the infrequency of such events in the data set. When analysing historical data, the AI model receives feedback on whether avalanches actually occurred. This feedback loop allows the model to learn from past events, refining its predictive accuracy. By comparing AI predictions with actual avalanche occurrences, researchers can continuously improve the model’s performance.

Human and Machine Synergy

Human forecasters use additional real-time observations, which are not available to the AI. However, the AI’s ability to process extensive simulations on the snow cover offers spatial and temporal resolutions beyond human capability. The combination of human intuition and machine precision creates a robust system for avalanche prediction.

Future Directions
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The SLF continues to refine this technology, aiming to better integrate human and machine forecasts. Enhancements include making the AI’s output more intuitive for avalanche warning services. This collaborative approach between AI and human experts represents a significant step forward in avalanche safety, promising more accurate and reliable forecasts.

By integrating AI into avalanche prediction, ski resorts and backcountry enthusiasts can look forward to improved safety measures, helping to mitigate the risks associated with this natural hazard.

For more details, visit SLF's official article.

For additional reading on how AI is transforming ski resorts, check out our article How Ski Resort Use Artificial Intelligence