Articles | Volume 25, issue 8
https://doi.org/10.5194/hess-25-4435-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-4435-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Aug 2021
Research article |
| 16 Aug 2021
| 16 Aug 2021
Deep learning for automated river-level monitoring through river-camera images: an approach based on water segmentation and transfer learning
Remy Vandaele
CORRESPONDING AUTHOR
Department of Meteorology, Meteorology Building, University of Reading, Reading RG6 6ET, UK
Department of Computer Sciences, Polly Vacher Building, Whiteknights, University of Reading, Reading RG6 6DH, UK
Sarah L. Dance
Department of Meteorology, Meteorology Building, University of Reading, Reading RG6 6ET, UK
Department of Mathematics and Statistics, Mathematics Building, Whiteknights, University of Reading, Reading RG6 6AX, UK
Varun Ojha
Department of Computer Sciences, Polly Vacher Building, Whiteknights, University of Reading, Reading RG6 6DH, UK
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Total article views: 3,894 (including HTML, PDF, and XML)
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Latest update: 20 Nov 2024
Short summary
The acquisition of river-level data is a critical task for the understanding of flood events but is often complicated by the difficulty to install and maintain gauges able to provide such information. This study proposes applying deep learning techniques on river-camera images in order to automatically extract the corresponding water levels. This approach could allow for a new flexible way to observe flood events, especially at ungauged locations.
The acquisition of river-level data is a critical task for the understanding of flood events but...
