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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- A Review of Non-Contact Water Level Measurement Based on Computer Vision and Radar Technology Z. Wu et al. 10.3390/w15183233
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- Predicting Dynamic Riverine Nitrogen Export in Unmonitored Watersheds: Leveraging Insights of AI from Data-Rich Regions R. Xiong et al. 10.1021/acs.est.2c02232
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- Evaluation of deep learning computer vision for water level measurements in rivers W. Liu & W. Huang 10.1016/j.heliyon.2024.e25989
- Advancing river monitoring using image-based techniques: challenges and opportunities S. Manfreda et al. 10.1080/02626667.2024.2333846
- Stage and discharge prediction from documentary time-lapse imagery K. Chapman et al. 10.1371/journal.pwat.0000106
- Application of Closed-Circuit Television Image Segmentation for Irrigation Channel Water Level Measurement K. Kim & J. Choi 10.3390/w15183308
- Deep Learning to Recognize Water Level for Agriculture Reservoir Using CCTV Imagery S. Kwon & S. Lee 10.1007/s11269-023-03714-7
- A deep learning workflow enhanced with optical flow fields for flood risk estimation C. Ranieri et al. 10.1007/s10489-024-05466-2
- A Comparative Performance Analysis of Popular Deep Learning Models and Segment Anything Model (SAM) for River Water Segmentation in Close-Range Remote Sensing Imagery A. Moghimi et al. 10.1109/ACCESS.2024.3385425
- Bringing vision to climate: A hierarchical model for water depth monitoring in headwater streams X. Zhao et al. 10.1016/j.inffus.2024.102448
- Cascade method for water level measurement based on computer vision D. Zhang & J. Qiu 10.1016/j.envsoft.2024.106285
- Deep learning for automated trash screen blockage detection using cameras: Actionable information for flood risk management R. Vandaele et al. 10.2166/hydro.2024.013
- Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera N. Muhadi et al. 10.3390/app11209691
- Identification of pedestrian submerged parts in urban flooding based on images and deep learning J. Jiang et al. 10.1016/j.envsoft.2024.106252
- Deep transfer learning for groundwater flow in heterogeneous aquifers using a simple analytical model J. Zhang et al. 10.1016/j.jhydrol.2023.130293
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Latest update: 13 Dec 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...
