Articles | Volume 17, issue 7
https://doi.org/10.5194/tc-17-2811-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-2811-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Jul 2023
Research article |
| 13 Jul 2023
| 13 Jul 2023
Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques
Ritu Anilkumar
CORRESPONDING AUTHOR
North Eastern Space Applications Centre, Department of Space, Umiam, Ri Bhoi, Meghalaya, India
Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India
Rishikesh Bharti
Department of Civil Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India
Dibyajyoti Chutia
North Eastern Space Applications Centre, Department of Space, Umiam, Ri Bhoi, Meghalaya, India
Shiv Prasad Aggarwal
North Eastern Space Applications Centre, Department of Space, Umiam, Ri Bhoi, Meghalaya, India
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Cited
20 citations as recorded by crossref.
- Uncertainty-Aware Learning With Label Noise for Glacier Mass Balance Modeling C. Diaconu & N. Gottschling https://doi.org/10.1109/LGRS.2024.3356160
- Remote Sensing and Graph Neural Network Reveal Past and Future Glacier Changes in Buha River Basin (1966–2100) X. Chu et al. https://doi.org/10.1109/JSTARS.2026.3693741
- Learning to melt: Emulating Greenland surface melt from a polar RCM with machine learning E. Schlager et al. https://doi.org/10.5194/tc-20-3313-2026
- Mass loss of Bayi Glacier in the Heihe River Basin revealed by ground-penetration radar measurements from 2006 to 2023 X. Pang et al. https://doi.org/10.1016/j.ejrh.2025.102255
- Machine learning for mapping glacier surface facies in Svalbard S. Wankhede et al. https://doi.org/10.1016/j.rsase.2025.101753
- Machine learning of Antarctic firn density by combining radiometer and scatterometer remote-sensing data W. Li et al. https://doi.org/10.5194/tc-19-37-2025
- Evaluating the affecting factors of glacier mass balance in Tanggula Mountains using explainable machine learning and the open global glacier model Q. Xu et al. https://doi.org/10.1007/s11629-024-9047-4
- GlaUnTI: a hybrid physics–machine learning model enables transferable glacier surface mass balance estimation K. Maslov et al. https://doi.org/10.5194/tc-20-6045-2026
- Physics-aware machine learning for glacier ice thickness estimation: a case study for Svalbard V. Steidl et al. https://doi.org/10.5194/tc-19-645-2025
- Emulating the expansion of Antarctic perennial firn aquifers in the 21st century S. Veldhuijsen et al. https://doi.org/10.5194/tc-19-5157-2025
- Evaluating the Performance of Multiple Machine Learning and Deep Learning Models on Glacier Mass Balance Estimation Y. Liao et al. https://doi.org/10.3390/sym18050873
- Automating glacier facies classification: Benchmark dataset and deep learning baseline from pan-European sample K. Maslov et al. https://doi.org/10.1016/j.srs.2026.100506
- Assessing glacial lake outburst flood risk in the Eastern Himalayas: a Bayesian neural network framework A. Vashistha et al. https://doi.org/10.1007/s11069-025-07668-4
- Comparison of Machine Learning Models in Simulating Glacier Mass Balance: Insights from Maritime and Continental Glaciers in High Mountain Asia W. Ren et al. https://doi.org/10.3390/rs16060956
- Snapshot and time-dependent inversions of basal sliding using automatic generation of adjoint code on graphics processing units I. Utkin et al. https://doi.org/10.1017/jog.2025.40
- A minimal machine-learning glacier mass balance model M. van der Meer et al. https://doi.org/10.5194/tc-19-805-2025
- Reconstructing ice phenology of a lake with complex surface cover: a case study of Lake Ulansu during 1941–2023 P. Huo et al. https://doi.org/10.5194/tc-19-849-2025
- Machine learning improves seasonal mass balance prediction for unmonitored glaciers K. Sjursen et al. https://doi.org/10.5194/tc-19-5801-2025
- Universal differential equations for glacier ice flow modelling J. Bolibar et al. https://doi.org/10.5194/gmd-16-6671-2023
- Machine Learning-Based probabilistic prediction of glacial lake formation using erosional and topographic features A. Vashistha et al. https://doi.org/10.1038/s41598-025-17401-7
20 citations as recorded by crossref.
- Uncertainty-Aware Learning With Label Noise for Glacier Mass Balance Modeling C. Diaconu & N. Gottschling https://doi.org/10.1109/LGRS.2024.3356160
- Remote Sensing and Graph Neural Network Reveal Past and Future Glacier Changes in Buha River Basin (1966–2100) X. Chu et al. https://doi.org/10.1109/JSTARS.2026.3693741
- Learning to melt: Emulating Greenland surface melt from a polar RCM with machine learning E. Schlager et al. https://doi.org/10.5194/tc-20-3313-2026
- Mass loss of Bayi Glacier in the Heihe River Basin revealed by ground-penetration radar measurements from 2006 to 2023 X. Pang et al. https://doi.org/10.1016/j.ejrh.2025.102255
- Machine learning for mapping glacier surface facies in Svalbard S. Wankhede et al. https://doi.org/10.1016/j.rsase.2025.101753
- Machine learning of Antarctic firn density by combining radiometer and scatterometer remote-sensing data W. Li et al. https://doi.org/10.5194/tc-19-37-2025
- Evaluating the affecting factors of glacier mass balance in Tanggula Mountains using explainable machine learning and the open global glacier model Q. Xu et al. https://doi.org/10.1007/s11629-024-9047-4
- GlaUnTI: a hybrid physics–machine learning model enables transferable glacier surface mass balance estimation K. Maslov et al. https://doi.org/10.5194/tc-20-6045-2026
- Physics-aware machine learning for glacier ice thickness estimation: a case study for Svalbard V. Steidl et al. https://doi.org/10.5194/tc-19-645-2025
- Emulating the expansion of Antarctic perennial firn aquifers in the 21st century S. Veldhuijsen et al. https://doi.org/10.5194/tc-19-5157-2025
- Evaluating the Performance of Multiple Machine Learning and Deep Learning Models on Glacier Mass Balance Estimation Y. Liao et al. https://doi.org/10.3390/sym18050873
- Automating glacier facies classification: Benchmark dataset and deep learning baseline from pan-European sample K. Maslov et al. https://doi.org/10.1016/j.srs.2026.100506
- Assessing glacial lake outburst flood risk in the Eastern Himalayas: a Bayesian neural network framework A. Vashistha et al. https://doi.org/10.1007/s11069-025-07668-4
- Comparison of Machine Learning Models in Simulating Glacier Mass Balance: Insights from Maritime and Continental Glaciers in High Mountain Asia W. Ren et al. https://doi.org/10.3390/rs16060956
- Snapshot and time-dependent inversions of basal sliding using automatic generation of adjoint code on graphics processing units I. Utkin et al. https://doi.org/10.1017/jog.2025.40
- A minimal machine-learning glacier mass balance model M. van der Meer et al. https://doi.org/10.5194/tc-19-805-2025
- Reconstructing ice phenology of a lake with complex surface cover: a case study of Lake Ulansu during 1941–2023 P. Huo et al. https://doi.org/10.5194/tc-19-849-2025
- Machine learning improves seasonal mass balance prediction for unmonitored glaciers K. Sjursen et al. https://doi.org/10.5194/tc-19-5801-2025
- Universal differential equations for glacier ice flow modelling J. Bolibar et al. https://doi.org/10.5194/gmd-16-6671-2023
- Machine Learning-Based probabilistic prediction of glacial lake formation using erosional and topographic features A. Vashistha et al. https://doi.org/10.1038/s41598-025-17401-7
Saved (final revised paper)
Latest update: 12 Nov 2026
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.
Our analysis demonstrates the capability of machine learning models in estimating glacier mass...
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