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.
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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Cited
22 citations as recorded by crossref.
- 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
- Deep learning methods for flood mapping: a review of existing applications and future research directions R. Bentivoglio et al. 10.5194/hess-26-4345-2022
- Raspberry Pi Reflector (RPR): A Low‐Cost Water‐Level Monitoring System Based on GNSS Interferometric Reflectometry M. Karegar et al. 10.1029/2021WR031713
- Urban flood extent segmentation and evaluation from real-world surveillance camera images using deep convolutional neural network Y. Wang et al. 10.1016/j.envsoft.2023.105939
- Temporary flood marks proposal: What we learned after losing the baroque artifact from Cracow, Poland R. Szczepanek et al. 10.1016/j.ijdrr.2022.102942
- A climate-adaptive transfer learning framework for improving soil moisture estimation in the Qinghai-Tibet Plateau J. Yang et al. 10.1016/j.jhydrol.2024.130717
- 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
- Eye of Horus: a vision-based framework for real-time water level measurement S. Erfani et al. 10.5194/hess-27-4135-2023
- Application of Closed-Circuit Television Image Segmentation for Irrigation Channel Water Level Measurement K. Kim & J. Choi 10.3390/w15183308
- Stochastic properties of coastal flooding events – Part 1: convolutional-neural-network-based semantic segmentation for water detection B. Kang et al. 10.5194/esurf-12-1-2024
- Deep Learning to Recognize Water Level for Agriculture Reservoir Using CCTV Imagery S. Kwon & S. Lee 10.1007/s11269-023-03714-7
- Uncertainty-aware transfer across tasks using hybrid model-based successor feature reinforcement learning☆ P. Malekzadeh et al. 10.1016/j.neucom.2023.01.076
- A Review of Non-Contact Water Level Measurement Based on Computer Vision and Radar Technology Z. Wu et al. 10.3390/w15183233
- Robust water level measurement method based on computer vision D. Zhang & J. Tong 10.1016/j.jhydrol.2023.129456
- Noncontact Automatic Water-Level Assessment and Prediction in an Urban Water Stream Channel of a Volcanic Island Using Deep Learning F. Mendonça et al. 10.3390/electronics13061145
- A review of video‐based rainfall measurement methods K. Yan et al. 10.1002/wat2.1678
- Calibrated river-level estimation from river cameras using convolutional neural networks R. Vandaele et al. 10.1017/eds.2023.6
- Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera N. Muhadi et al. 10.3390/app11209691
- Memory-Based Pruning of Deep Neural Networks for IoT Devices Applied to Flood Detection F. Fernandes Junior et al. 10.3390/s21227506
- Assessment of a Machine Learning Algorithm Using Web Images for Flood Detection and Water Level Estimates M. Tedesco & J. Radzikowski 10.3390/geohazards4040025
- Deep transfer learning for groundwater flow in heterogeneous aquifers using a simple analytical model J. Zhang et al. 10.1016/j.jhydrol.2023.130293
22 citations as recorded by crossref.
- 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
- Deep learning methods for flood mapping: a review of existing applications and future research directions R. Bentivoglio et al. 10.5194/hess-26-4345-2022
- Raspberry Pi Reflector (RPR): A Low‐Cost Water‐Level Monitoring System Based on GNSS Interferometric Reflectometry M. Karegar et al. 10.1029/2021WR031713
- Urban flood extent segmentation and evaluation from real-world surveillance camera images using deep convolutional neural network Y. Wang et al. 10.1016/j.envsoft.2023.105939
- Temporary flood marks proposal: What we learned after losing the baroque artifact from Cracow, Poland R. Szczepanek et al. 10.1016/j.ijdrr.2022.102942
- A climate-adaptive transfer learning framework for improving soil moisture estimation in the Qinghai-Tibet Plateau J. Yang et al. 10.1016/j.jhydrol.2024.130717
- 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
- Eye of Horus: a vision-based framework for real-time water level measurement S. Erfani et al. 10.5194/hess-27-4135-2023
- Application of Closed-Circuit Television Image Segmentation for Irrigation Channel Water Level Measurement K. Kim & J. Choi 10.3390/w15183308
- Stochastic properties of coastal flooding events – Part 1: convolutional-neural-network-based semantic segmentation for water detection B. Kang et al. 10.5194/esurf-12-1-2024
- Deep Learning to Recognize Water Level for Agriculture Reservoir Using CCTV Imagery S. Kwon & S. Lee 10.1007/s11269-023-03714-7
- Uncertainty-aware transfer across tasks using hybrid model-based successor feature reinforcement learning☆ P. Malekzadeh et al. 10.1016/j.neucom.2023.01.076
- A Review of Non-Contact Water Level Measurement Based on Computer Vision and Radar Technology Z. Wu et al. 10.3390/w15183233
- Robust water level measurement method based on computer vision D. Zhang & J. Tong 10.1016/j.jhydrol.2023.129456
- Noncontact Automatic Water-Level Assessment and Prediction in an Urban Water Stream Channel of a Volcanic Island Using Deep Learning F. Mendonça et al. 10.3390/electronics13061145
- A review of video‐based rainfall measurement methods K. Yan et al. 10.1002/wat2.1678
- Calibrated river-level estimation from river cameras using convolutional neural networks R. Vandaele et al. 10.1017/eds.2023.6
- Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera N. Muhadi et al. 10.3390/app11209691
- Memory-Based Pruning of Deep Neural Networks for IoT Devices Applied to Flood Detection F. Fernandes Junior et al. 10.3390/s21227506
- Assessment of a Machine Learning Algorithm Using Web Images for Flood Detection and Water Level Estimates M. Tedesco & J. Radzikowski 10.3390/geohazards4040025
- Deep transfer learning for groundwater flow in heterogeneous aquifers using a simple analytical model J. Zhang et al. 10.1016/j.jhydrol.2023.130293
Latest update: 28 Mar 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...