Articles | Volume 22, issue 11
Hydrol. Earth Syst. Sci., 22, 5639–5656, 2018
https://doi.org/10.5194/hess-22-5639-2018

Special issue: HESS Opinions 2018

Hydrol. Earth Syst. Sci., 22, 5639–5656, 2018
https://doi.org/10.5194/hess-22-5639-2018

Opinion article 01 Nov 2018

Opinion article | 01 Nov 2018

HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community

Chaopeng Shen et al.

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Interactive discussion

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: Reconsider after major revisions (further review by editor and referees) (22 Jul 2018) by Louise Slater
AR by Chaopeng Shen on behalf of the Authors (02 Sep 2018)  Author's response    Manuscript
ED: Publish subject to technical corrections (19 Sep 2018) by Louise Slater
AR by Chaopeng Shen on behalf of the Authors (25 Sep 2018)  Author's response    Manuscript
Special issue
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Short summary
Recently, deep learning (DL) has emerged as a revolutionary tool for transforming industries and scientific disciplines. We argue that DL can offer a complementary avenue toward advancing hydrology. New methods are being developed to interpret the knowledge learned by deep networks. We argue that open competitions, integrating DL and process-based models, more data sharing, data collection from citizen scientists, and improved education will be needed to incubate advances in hydrology.