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Chapitre D'ouvrage Année : 2022

A Multilevel Clustering Method for Risky Areas in the Context of Avalanche Danger Management

Résumé

In the context of avalanche risk management, we study the spatial variability of rainfall conditions, which is one of the main parameters that induce natural avalanches. This paper focuses on the geographical variability of the snow overload due to recent precipitations. We propose a generic approach applicable at a larger scale and, for this reason, without relying on any expert knowledge. Our proposal is a multilevel clustering process based on classical methods processed in sequence to take advantage of each one. As a result, the multilevel clustering process outputs four main detected weather trends that affect the French Alps. The developed process is generic enough to be used in other areas. Our work is intended to positively impact and improve the current and future decision support methods and tools for mountain practitioners.
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Dates et versions

hal-03728237 , version 1 (20-07-2022)

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Fanny Pagnier, Frédéric Pourraz, Didier Coquin, Gilles Mauris, Hervé Verjus. A Multilevel Clustering Method for Risky Areas in the Context of Avalanche Danger Management. Information Processing and Management of Uncertainty in Knowledge-Based Systems, 1602, Springer International Publishing, pp.54-68, 2022, Communications in Computer and Information Science, ⟨10.1007/978-3-031-08974-9_5⟩. ⟨hal-03728237⟩

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