Assessing the predictive capability of several machine learning algorithms to forecast snow avalanches using numerical weather prediction model in eastern Canada

Gauthier, Francis; Laliberté, Jacob; Meloche, Francis

doi:10.5194/nhess-25-5033-2025

Articles | Volume 25, issue 12

https://doi.org/10.5194/nhess-25-5033-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/nhess-25-5033-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 12

Research article

|

19 Dec 2025

Research article |

| 19 Dec 2025

Assessing the predictive capability of several machine learning algorithms to forecast snow avalanches using numerical weather prediction model in eastern Canada

Francis Gauthier, Jacob Laliberté, and Francis Meloche

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Final revised paper (published on 19 Dec 2025)
Preprint (discussion started on 10 Apr 2025)

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-1572', Frank Techel, 17 Apr 2025

Please find my comments in the attached document.

Citation: https://doi.org/10.5194/egusphere-2025-1572-RC1
- AC1: 'Reply on RC1', francis meloche, 12 Sep 2025
  
  We would like to sincerely thank the reviewer for their thoughtful and constructive comments, which have helped us improve the clarity and quality of our manuscript. A detailed point-by-point response to each of the reviewer’s comments is provided in the attached document.
  
  Citation: https://doi.org/10.5194/egusphere-2025-1572-AC1
RC2:
'Comment on egusphere-2025-1572', Erich Peitzsch, 22 May 2025

Please see the attached document for comments.

Citation: https://doi.org/10.5194/egusphere-2025-1572-RC2
- AC2: 'Reply on RC2', francis meloche, 12 Sep 2025
  
  We would like to sincerely thank the reviewer for their thoughtful and constructive comments, which have helped us improve the clarity and quality of our manuscript. A detailed point-by-point response to each of the reviewer’s comments is provided in the attached document.
  
  Citation: https://doi.org/10.5194/egusphere-2025-1572-AC2
RC3:
'Comment on egusphere-2025-1572', Cristina Pérez-Guillén, 26 May 2025

The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1572/egusphere-2025-1572-RC3-supplement.pdf

Citation: https://doi.org/10.5194/egusphere-2025-1572-RC3
- AC3: 'Reply on RC3', francis meloche, 12 Sep 2025
  
  We would like to sincerely thank the reviewer for their thoughtful and constructive comments, which have helped us improve the clarity and quality of our manuscript. A detailed point-by-point response to each of the reviewer’s comments is provided in the attached document.
  
  Citation: https://doi.org/10.5194/egusphere-2025-1572-AC3

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload

ED: Reconsider after major revisions (further review by editor and referees) (15 Sep 2025) by Yves Bühler

AR by francis meloche on behalf of the Authors (17 Sep 2025) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (18 Sep 2025) by Yves Bühler

RR by Erich Peitzsch (04 Oct 2025)

ED: Publish as is (04 Oct 2025) by Yves Bühler

AR by francis meloche on behalf of the Authors (13 Oct 2025)

Short summary

This study uses 4 different machine learning (ML) methods to forecast snow avalanches in northern Gaspésie using Québec's Ministry of Transportation avalanche records, and meteorological data. Comparing unsupervised and expert-driven models, results show similar prediction accuracy. Logistic Regression and Random Forest models perform well in real-time forecasting over 24–48 h. Findings suggest ML can enhance avalanche hazard anticipation and support operational decision-making.