Articles | Volume 22, issue 11
https://doi.org/10.5194/hess-22-5639-2018
Special issue:
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, Eric Laloy, Amin Elshorbagy, Adrian Albert, Jerad Bales, Fi-John Chang, Sangram Ganguly, Kuo-Lin Hsu, Daniel Kifer, Zheng Fang, Kuai Fang, Dongfeng Li, Xiaodong Li, and Wen-Ping Tsai

Download

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

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 
Download
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.
Special issue