Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 30, issue 3
Articles | Volume 30, issue 3
https://doi.org/10.5194/hess-30-797-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-797-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 30, issue 3
Research article
 | 
12 Feb 2026
Research article |  | 12 Feb 2026

AI image-based method for a robust automatic real-time water level monitoring: a long-term application case

Xabier Blanch, Jens Grundmann, Ralf Hedel, and Anette Eltner

Viewed

Total article views: 1,957 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,451 453 53 1,957 42 62
  • HTML: 1,451
  • PDF: 453
  • XML: 53
  • Total: 1,957
  • BibTeX: 42
  • EndNote: 62
Views and downloads (calculated since 27 Mar 2025)
Cumulative views and downloads (calculated since 27 Mar 2025)

Viewed (geographical distribution)

Total article views: 1,957 (including HTML, PDF, and XML) Thereof 1,908 with geography defined and 49 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 12 Feb 2026
Download
Short summary
This study presents a low-cost, automated system for monitoring river water levels using cameras and AI. By combining AI-based image analysis with photogrammetry, it accurately measures water levels in real-time, even in challenging conditions. Tested over 2.5 years at four sites, it achieved high accuracy (errors of 1.0–2.3 cm) and processed over 219 000 images. Its resilience makes it ideal for flood detection and water management in remote areas.
Read more
Share