A random forest model to assess snow instability from simulated snow stratigraphy

Abstract P unstable N = 121) with high reliability (accuracy 88 %, precision 96 %, recall 85 %) using manually predefined weak layers. Model performance was even higher (accuracy 93 %, precision 96 %, recall 92 %), when the weakest layers of the profiles were identified with the maximum of P unstable. Finally, we compared model predictions to observed avalanche activity in the region of Davos for five winter seasons. Of the 252 avalanche days (345 non-avalanche days), 69 % (75 %) were classified correctly. Overall, the results of our RF classification are very encouraging, suggesting it could be of great value for operational avalanche forecasting.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: A random forest model to assess snow instability from simulated snow stratigraphy ; volume:16 ; number:11 ; year:2022 ; pages:4593-4615 ; extent:23 ; The Cryosphere ; 16, Heft 11 (2022), 4593-4615 (gesamt 23)

Contributors, Places and Time

• Creator: Mayer, Stephanie ; van Herwijnen, Alec ; Techel, Frank ; Schweizer, Jürg

Further information

• DOI: 10.5194/tc-16-4593-2022
• URN: urn:nbn:de:101:1-2022111004354715912250
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:20 AM CEST