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The Cryosphere An interactive open-access journal of the European Geosciences Union
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TC | 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.

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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to revisions (further review by editor and referees) (01 Nov 2019) by Valentina Radic
AR by Jordi Bolibar on behalf of the Authors (03 Dec 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (04 Dec 2019) by Valentina Radic
RR by Fabien Maussion (24 Dec 2019)
ED: Publish subject to minor revisions (review by editor) (14 Jan 2020) by Valentina Radic
AR by Jordi Bolibar on behalf of the Authors (16 Jan 2020)  Author's response    Manuscript
ED: Publish as is (18 Jan 2020) by Valentina Radic
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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