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

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

doi:10.5194/hess-30-797-2026

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

Data sets

River Water Segmentation Dataset (RIWA) Xabier Blanch et al. https://doi.org/10.34740/kaggle/dsv/4901781

Model code and software

KIWA Software for Water Level (wl_opt) - KIWA Project Xabier Blanch https://doi.org/10.5281/zenodo.17675672

Video supplement

Water level results at Lauenstein gauge station - KIWA Project Xabier Blanch et al. https://doi.org/10.5281/zenodo.14875801

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.