DEAPSnow – Improving snow avalanche forecasting by data-driven automated predictions

Co-PIs:

  • Jürg Schweizer, SLF
  • Alec van Herwijnen, SLF

Objectives:

The overall goal of the proposed research is to improve avalanche forecasting by developing a decision support tool that provides data-driven automated predictions of avalanche hazard. We hypothesize that by applying modern data science and machine learning methods on the diverse (in time and space) snow and avalanche data, snow avalanche hazard can automatically be forecast – with at least the accuracy of present experience-based forecasts.