Deep Learning and Optical Flow for River Velocity Estimation: Insights from a Field Case Study

Walter Chen (), Kieu Anh Nguyen and Bor-Shiun Lin
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Walter Chen: Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
Kieu Anh Nguyen: Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
Bor-Shiun Lin: Ultron Technology Engineering Company, Taipei 11072, Taiwan

Sustainability, 2025, vol. 17, issue 18, 1-21

Abstract: Accurate river flow velocity estimation is critical for flood risk management and sediment transport modeling. This study proposes an artificial intelligence (AI)-based framework that integrates optical flow analysis and deep learning to estimate flow velocity from charge-coupled device (CCD) camera videos. The approach was tested on a field dataset from Yufeng No. 2 stream (torrent), consisting of 3263 ten min 4 K videos recorded over two months, paired with Doppler radar measurements as the ground truth. Video preprocessing included frame resizing to 224 × 224 pixels, day/night classification, and exclusion of sequences with missing frames. Two deep learning architectures—a convolutional neural network combined with long short-term memory (CNN+LSTM) and a three-dimensional convolutional neural network (3D CNN)—were evaluated under different input configurations: red–green–blue (RGB) frames, optical flow, and combined RGB with optical flow. Performance was assessed using Nash–Sutcliffe Efficiency (NSE) and the index of agreement ( d statistic). Results show that optical flow combined with a 3D CNN achieved the best accuracy (NSE > 0.5), outperforming CNN+LSTM and RGB-based inputs. Increasing the training set beyond approximately 100 videos provided no significant improvement, while nighttime videos degraded performance due to poor image quality and frame loss. These findings highlight the potential of combining optical flow and deep learning for cost-effective and scalable flow monitoring in small rivers. Future work will address nighttime video enhancement, broader velocity ranges, and real-time implementation. By improving the timeliness and accuracy of river flow monitoring, the proposed approach supports early warning systems, flood risk reduction, and sustainable water resource management. When integrated with turbidity measurements, it enables more accurate estimation of sediment loads transported into downstream reservoirs, helping to predict siltation rates and safeguard long-term water supply capacity. These outcomes contribute to the Sustainable Development Goals, particularly SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action), by enhancing disaster preparedness, protecting communities, and promoting climate-resilient water management practices.

Keywords: river flow velocity; optical flow; deep learning; convolutional neural network; long short-term memory; three-dimensional convolutional neural network; video-based flow measurement; flood risk management; sediment transport; torrential streams (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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