Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 28, issue 17
Articles | Volume 28, issue 17
https://doi.org/10.5194/hess-28-4085-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-4085-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 28, issue 17
Technical note
 | 
10 Sep 2024
Technical note |  | 10 Sep 2024

Technical note: Monitoring discharge of mountain streams by retrieving image features with deep learning

Chenqi Fang, Genyu Yuan, Ziying Zheng, Qirui Zhong, and Kai Duan

Viewed

Total article views: 864 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
623 183 58 864 46 38
  • HTML: 623
  • PDF: 183
  • XML: 58
  • Total: 864
  • BibTeX: 46
  • EndNote: 38
Views and downloads (calculated since 05 May 2023)
Cumulative views and downloads (calculated since 05 May 2023)

Viewed (geographical distribution)

Total article views: 864 (including HTML, PDF, and XML) Thereof 870 with geography defined and -6 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 26 Sep 2024
Download
Short summary
Measuring discharge at steep, rocky mountain streams is challenging due to the difficulties in identifying cross-section characteristics and establishing stable stage–discharge relationships. We present a novel method using only a low-cost commercial camera and deep learning algorithms. Our study shows that deep convolutional neural networks can automatically recognize and retrieve complex stream features embedded in RGB images to achieve continuous discharge monitoring.
Read more