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

Viewed

Total article views: 2,885 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,251 554 80 2,885 106 91 120
  • HTML: 2,251
  • PDF: 554
  • XML: 80
  • Total: 2,885
  • Supplement: 106
  • BibTeX: 91
  • EndNote: 120
Views and downloads (calculated since 10 Nov 2022)
Cumulative views and downloads (calculated since 10 Nov 2022)

Viewed (geographical distribution)

Total article views: 2,885 (including HTML, PDF, and XML) Thereof 2,828 with geography defined and 57 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Dec 2025
Download
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.
Share