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: 1,179 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
805 338 36 1,179 68 27 19
  • HTML: 805
  • PDF: 338
  • XML: 36
  • Total: 1,179
  • Supplement: 68
  • BibTeX: 27
  • EndNote: 19
Views and downloads (calculated since 10 Nov 2022)
Cumulative views and downloads (calculated since 10 Nov 2022)

Viewed (geographical distribution)

Total article views: 1,179 (including HTML, PDF, and XML) Thereof 1,151 with geography defined and 28 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 01 Mar 2024
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.