12 citations as recorded by crossref.

1. Spatiotemporal mass-balance variability of Jostedalsbreen Ice Cap, Norway, revealed by a temperature-index model using Bayesian inference K. Sjursen et al. 10.1017/aog.2024.41
2. A combined data assimilation and deep learning approach for continuous spatio-temporal SWE reconstruction from sparse ground tracks M. Guidicelli et al. 10.1016/j.hydroa.2024.100190
3. Prediction of glaciated area fraction over the Sikkim Himalayan Region, India: a comparative study using response surface method, random forest, and artificial neural network S. Kumari & A. Middey 10.1007/s10661-023-11770-0
4. Glacier projections sensitivity to temperature-index model choices and calibration strategies L. Schuster et al. 10.1017/aog.2023.57
5. 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. 10.1007/s11629-024-9047-4
6. Universal differential equations for glacier ice flow modelling J. Bolibar et al. 10.5194/gmd-16-6671-2023
7. Uncertainty-Aware Learning With Label Noise for Glacier Mass Balance Modeling C. Diaconu & N. Gottschling 10.1109/LGRS.2024.3356160
8. Artificial intelligence for geoscience: Progress, challenges, and perspectives T. Zhao et al. 10.1016/j.xinn.2024.100691
9. Performance Evaluation of High-resolution Reanalysis Datasets Over North-central British Columbia U. Goswami et al. 10.1080/07055900.2024.2308878
10. Application of Artificial Intelligence in the Assessment and Forecast of Avalanche Danger in the Ile Alatau Ridge V. Blagovechshenskiy et al. 10.3390/w15071438
11. Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques R. Anilkumar et al. 10.5194/tc-17-2811-2023
12. Glacial retreat delineation using machine and deep learning: A case of a lower Himalayan region S. Vemuri et al. 10.1007/s12040-024-02285-4