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

Viewed

Total article views: 9,689 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
7,016 2,575 98 9,689 123 124
  • HTML: 7,016
  • PDF: 2,575
  • XML: 98
  • Total: 9,689
  • BibTeX: 123
  • EndNote: 124
Views and downloads (calculated since 09 Apr 2018)
Cumulative views and downloads (calculated since 09 Apr 2018)

Viewed (geographical distribution)

Total article views: 9,689 (including HTML, PDF, and XML) Thereof 8,816 with geography defined and 873 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Discussed (final revised paper)

Latest update: 20 Nov 2024
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