Articles | Volume 17, issue 7
https://doi.org/10.5194/tc-17-2811-2023
https://doi.org/10.5194/tc-17-2811-2023
Research article
 | 
13 Jul 2023
Research article |  | 13 Jul 2023

Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques

Ritu Anilkumar, Rishikesh Bharti, Dibyajyoti Chutia, and Shiv Prasad Aggarwal

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Review of Anilkumar et al.', Jordi Bolibar, 20 Dec 2022
    • AC1: 'Reply on RC1', Ritu Anilkumar, 11 Jan 2023
  • RC2: 'Comment on egusphere-2022-1076', Anonymous Referee #2, 21 Feb 2023
    • AC2: 'Reply on RC2', Ritu Anilkumar, 15 Mar 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to revisions (further review by editor and referees) (16 Mar 2023) by Emily Collier
AR by Ritu Anilkumar on behalf of the Authors (26 Apr 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to minor revisions (review by editor) (02 Jun 2023) by Emily Collier
AR by Ritu Anilkumar on behalf of the Authors (08 Jun 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (12 Jun 2023) by Emily Collier
AR by Ritu Anilkumar on behalf of the Authors (13 Jun 2023)  Author's response   Manuscript 
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Short summary
Our analysis demonstrates the capability of machine learning models in estimating glacier mass balance in terms of performance metrics and dataset availability. Feature importance analysis suggests that ablation features are significant. This is in agreement with the predominantly negative mass balance observations. We show that ensemble tree models typically depict the best performance. However, neural network models are preferable for biased inputs and kernel-based models for smaller datasets.