Item: INTEGRATING AUTOMATED AVALANCHE DETECTIONS FOR VALIDATING AND EXPLAINING AVALANCHE FORECASTING MODELS
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Title: INTEGRATING AUTOMATED AVALANCHE DETECTIONS FOR VALIDATING AND EXPLAINING AVALANCHE FORECASTING MODELS
Proceedings: International Snow Science Workshop 2024, Tromsø, Norway
Authors:
- Cristina Pérez-Guillén [ WSL Institute for Snow and Avalanche Research SLF, Switzerland ]
- Andri Simeon [ WSL Institute for Snow and Avalanche Research SLF, Switzerland ]
- Frank Techel [ WSL Institute for Snow and Avalanche Research SLF, Switzerland ]
- Michele Volpi [ Swiss Data Science Center, ETH Zurich and EPFL, Switzerland ]
- Betty Sovilla [ WSL Institute for Snow and Avalanche Research SLF, Switzerland ]
- Alec van Herwijnen [ WSL Institute for Snow and Avalanche Research SLF, Switzerland ]
Date: 2024-09-23
Abstract: Snow avalanches are the deadliest natural hazards in Switzerland and cause significant economic losses annually. Therefore, avalanche detection and prediction are crucial in ensuring safety and mobility in the Swiss Alps. Operationally, avalanche forecasts are issued daily during the winter season to inform the public and local authorities about the avalanche hazard. Whereas traditional avalanche forecasting has been a human-expert decision-making process entirely, machine-learning models have become increasingly used in recent years. In this context, the Swiss avalanche warning service has been at the forefront of using these innovative data-driven tools to supplement current forecasting practices. These data-driven approaches have shown the potential to provide objective decision tools with higher spatiotemporal resolution than human-based forecasts. Nevertheless, the predictions generated by these machine learning models are opaque, usually referred to as black boxes, making it challenging for avalanche forecasters to interpret how variables are combined to make predictions. To address this, we employed an explanation method to interpret predictions from a machine learning model developed to predict danger levels for dry snow conditions in Switzerland. Moreover, precise avalanche data collected from an automated seismic detection system at the Vallée de la Sionne test site (Switzerland) are used to validate these danger level forecasts during the winter season 2020-2021. A novelty in future model implementations would be integrating these timely avalanche detections into forecasting models.
Object ID: ISSW2024_O1.9.pdf
Language of Article: English
Presenter(s): Cristina Pérez-Guillén
Keywords: Avalanche Forecasting, Automatic Avalanche detection, Machine Learning Models
Page Number(s): 52 - 57
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