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

Related authors

Seismic Deformation of Himalayan Glaciers Using Synthetic Aperture Radar Interferometry
Sandeep Kumar Mondal, Rishikesh Bharti, and Kristy F. Tiampo
EGUsphere, https://doi.org/10.5194/egusphere-2023-2253,https://doi.org/10.5194/egusphere-2023-2253, 2023
Short summary
TIME SERIES ANALYSIS OF URBANISATION IMPACT ON THE TEMPERATURE VARIATIONS OFF MUMBAI COAST
S. Bhattacharjee, K. Lekshmi, and R. Bharti
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 31–37, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-31-2021,https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-31-2021, 2021
PROMOTING INTERNATIONAL COLLABORATION THROUGH TRAINING AND EDUCATION IN SPACE TECHNOLOGY APPLICATIONS AND ADVANCES AMONG BIMSTEC COUNTRIES – A GOVERNMENT OF INDIA INITIATIVE
P. L. N. Raju, D. Chutia, N. Nishant, J. Goswami, and R. Anil Kumar
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B5-2020, 29–34, https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-29-2020,https://doi.org/10.5194/isprs-archives-XLIII-B5-2020-29-2020, 2020

Related subject area

Discipline: Glaciers | Subject: Alpine Glaciers
Brief Communication: Recent estimates of glacier mass loss for western North America from laser altimetry
Brian Menounos, Alex Gardner, Caitlyn Forentine, and Andrew Fountain
EGUsphere, https://doi.org/10.5194/egusphere-2023-2408,https://doi.org/10.5194/egusphere-2023-2408, 2023
Short summary
The Aneto glacier's (Central Pyrenees) evolution from 1981 to 2022: ice loss observed from historic aerial image photogrammetry and remote sensing techniques
Ixeia Vidaller, Eñaut Izagirre, Luis Mariano del Rio, Esteban Alonso-González, Francisco Rojas-Heredia, Enrique Serrano, Ana Moreno, Juan Ignacio López-Moreno, and Jesús Revuelto
The Cryosphere, 17, 3177–3192, https://doi.org/10.5194/tc-17-3177-2023,https://doi.org/10.5194/tc-17-3177-2023, 2023
Short summary
Consistent histories of anthropogenic western European air pollution preserved in different Alpine ice cores
Anja Eichler, Michel Legrand, Theo M. Jenk, Susanne Preunkert, Camilla Andersson, Sabine Eckhardt, Magnuz Engardt, Andreas Plach, and Margit Schwikowski
The Cryosphere, 17, 2119–2137, https://doi.org/10.5194/tc-17-2119-2023,https://doi.org/10.5194/tc-17-2119-2023, 2023
Short summary
Brief communication: Non-linear sensitivity of glacier mass balance to climate attested by temperature-index models
Christian Vincent and Emmanuel Thibert
The Cryosphere, 17, 1989–1995, https://doi.org/10.5194/tc-17-1989-2023,https://doi.org/10.5194/tc-17-1989-2023, 2023
Short summary
Halving of Swiss glacier volume since 1931 observed from terrestrial image photogrammetry
Erik Schytt Mannerfelt, Amaury Dehecq, Romain Hugonnet, Elias Hodel, Matthias Huss, Andreas Bauder, and Daniel Farinotti
The Cryosphere, 16, 3249–3268, https://doi.org/10.5194/tc-16-3249-2022,https://doi.org/10.5194/tc-16-3249-2022, 2022
Short summary

Cited articles

Altmann, A., Toloşi, L., Sander, O., and Lengauer, T.: Permutation importance: a corrected feature importance measure, Bioinformatics, 26, 1340–1347, https://doi.org/10.1093/bioinformatics/btq134, 2010. a, b
Anilkumar, R., Bharti, R., and Chutia, D.: Point Mass Balance Regression using Deep Neural Networks: A Transfer Learning Approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5317, https://doi.org/10.5194/egusphere-egu22-5317, 2022. a
Bair, E. H., Abreu Calfa, A., Rittger, K., and Dozier, J.: Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan, The Cryosphere, 12, 1579–1594, https://doi.org/10.5194/tc-12-1579-2018, 2018. a
Bash, E. A., Moorman, B. J., and Gunther, A.: Detecting Short-Term Surface Melt on an Arctic Glacier Using UAV Surveys, Remote Sensing, 10, 1547, https://doi.org/10.3390/rs10101547, 2018. a
Bolibar, J., Rabatel, A., Gouttevin, I., Galiez, C., Condom, T., and Sauquet, E.: Deep learning applied to glacier evolution modelling, The Cryosphere, 14, 565–584, https://doi.org/10.5194/tc-14-565-2020, 2020. a, b, c, d
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.