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The Cryosphere An interactive open-access journal of the European Geosciences Union
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TC | Articles | Volume 14, issue 2
The Cryosphere, 14, 565–584, 2020
https://doi.org/10.5194/tc-14-565-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
The Cryosphere, 14, 565–584, 2020
https://doi.org/10.5194/tc-14-565-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 13 Feb 2020

Research article | 13 Feb 2020

Deep learning applied to glacier evolution modelling

Jordi Bolibar et al.

Model code and software

JordiBolibar/ALPGM: ALPGM v1.1 J. Bolibar https://doi.org/10.5281/zenodo.3609136

Publications Copernicus
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Short summary
We introduce a novel approach for simulating glacier mass balances using a deep artificial neural network (i.e. deep learning) from climate and topographical data. This has been added as a component of a new open-source parameterized glacier evolution model. Deep learning is found to outperform linear machine learning methods, mainly due to its nonlinearity. Potential applications range from regional mass balance reconstructions from observations to simulations for past and future climates.
We introduce a novel approach for simulating glacier mass balances using a deep artificial...
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