
DEAPSnow
Improving snow avalanche forecasting by data-driven automated predictions

Abstract
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 hypothesise 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.
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Fernando received a PhD. in Electrical Engineering from the Technical University of Madrid. He has been a member of the technical staff at Bell Labs and a Machine Learning Research Scientist at Amazon. Fernando has been a visiting professor at Princeton University under a Marie Curie Fellowship and an associate professor at University Carlos III in Madrid. He held positions at the Gatsby Unit (London), Max Planck Institute for Biological Cybernetics (Tuebingen), and BioWulf Technologies (New York). Since 2022, Fernando is the Deputy Executive Director of the SDSC.


Guillaume Obozinski graduated with a PhD in Statistics from UC Berkeley in 2009. He did his postdoc and held until 2012 a researcher position in the Willow and Sierra teams at INRIA and Ecole Normale Supérieure in Paris. He was then Research Faculty at Ecole des Ponts ParisTech until 2018. Guillaume has broad interests in statistics and machine learning and worked over time on sparse modeling, optimization for large scale learning, graphical models, relational learning and semantic embeddings, with applications in various domains from computational biology to computer vision.


Michele received a Ph.D. in Environmental Sciences from the University of Lausanne (Switzerland) in 2013. He was then a visiting postdoc in the CALVIN group, Institute of Perception, Action and Behaviour of the School of Informatics at the University of Edinburgh, Scotland (2014-2016). He then joined the Multimodal Remote Sensing and the Geocomputation groups at the Geography department of the University of Zurich, Switzerland (2016-2017). His main research activities were at the interface of computer vision, machine and deep learning for the extraction of information from aerial photos, satellite optical images and geospatial data in general.
Snow Avalanches and Prevention:
- Prof. Jürg Schweizer
- Dr. Alec van Herwijnen
- Dr. Martin Hendrick
- Dr. Cristina Pérez Guillén
- Dr. Frank Techel
description
Problem:
Operational avalanche forecasting – issuing warnings to the general public – is still by and large an experienced-based process. The lack of appropriate numerical or statistical methods has hence prevented:
- Knowledge extraction required for a sustainable operation.
- Numerical forecasting i.e. data-driven decision support crucially important for consistent and objective forecasts.
Proposed approach:
- Develop statistical learning techniques for support decision making in snow and avalanche disciplines
- Develop data-driven probabilistic methods from multi-year snow and avalanche datasets to support operational avalanche forecasting
Impact:
- Assist avalanche forecasters and operational decision support tools with:
- Predictive methods of avalanche danger level (a score from 1 to 5)
- Predictive models of avalanche danger type (the type of likely avalanches)
- A better understanding of the spatio-temporal relationships between measurement stations, and potentially improve the monitoring network
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