Articles | Volume 14, issue 7
Geosci. Model Dev., 14, 4495–4508, 2021
https://doi.org/10.5194/gmd-14-4495-2021
Geosci. Model Dev., 14, 4495–4508, 2021
https://doi.org/10.5194/gmd-14-4495-2021

Methods for assessment of models 22 Jul 2021

Methods for assessment of models | 22 Jul 2021

Testing the reliability of interpretable neural networks in geoscience using the Madden–Julian oscillation

Benjamin A. Toms et al.

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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Mario Ebel on behalf of the Authors (22 Jan 2021)  Author's response
ED: Publish as is (13 Feb 2021) by Richard Neale
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Short summary
We test whether a type of machine learning called neural networks can be used trustfully within the geosciences. We do so by challenging the networks to understand the spatial patterns of a commonly studied geoscientific phenomenon. The neural networks can correctly identify the spatial patterns, which lends confidence that similar networks can be used for more uncertain problems. The results of this study may give geoscientists confidence when using neural networks in their research.