Item: DATA-DRIVEN MODELS USED IN OPERATIONAL AVALANCHE FORECASTING IN SWITZERLAND
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Title: DATA-DRIVEN MODELS USED IN OPERATIONAL AVALANCHE FORECASTING IN SWITZERLAND
Proceedings: International Snow Science Workshop Proceedings 2023, Bend, Oregon
Authors:
- Alec van Herwijnen [ WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland ]
- Stephanie Mayer [ WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland ]
- Cristina Pérez Guillén [ WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland ]
- Frank Techel [ WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland ]
- Martin Hendrick [ WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland ]
- Jürg Schweizer [ WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland ]
Date: 2023-10-08
Abstract: One of the main challenges in avalanche forecasting is the complexity of the snowpack and its interactions with the environment. Traditional methods rely on expert knowledge to analyze snow and weather data, field observations and weather predictions, which is time-consuming and partly subjective. In contrast, data-driven models can analyze large amounts of data faster and may identify patterns that are difficult for humans to detect. Such models are based on statistical or machine learning algorithms that learn from past data to make predictions about new situations. Data-driven models are increasingly used in avalanche forecasting, as they can provide more objective and timely predictions, assisting forecasters in decision-making. Here, we present three recently developed models used in operational avalanche forecasting in Switzerland. The data-driven models use machine-learning algorithms with meteorological and simulated snow stratigraphy data as input to predict (1) the avalanche danger level, (2) snowpack instability and natural avalanche probability, and (3) wet-snow avalanche probability. The models were trained on historical data and typically have an accuracy of about 75\%. During the last three winter seasons, we tested these models in operational avalanche forecasting for the Swiss Alps at SLF. Models 1 and 2 were consulted daily, while model 3 only in potential wet-snow avalanche situations. Preliminary results suggest that the models performed equally well in nowcast mode, when driven with measured data, as in forecast mode, when driven with data from numerical weather prediction models. Overall, the positive feedback we received from the forecasters shows that data-driven models can successfully be integrated into operational forecasting systems.
Object ID: ISSW2023_P1.33.pdf
Language of Article: English
Presenter(s): Alec van Herwijnen
Keywords: avalanche forecasting, machine learning, data-driven models
Page Number(s): 321 - 326
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