Wang et al. (2025) Improving river surface flow velocity measurement by coupling optimal search line algorithm with space-time image velocimetry

Identification

• Journal: Journal of Hydrology
• Year: 2025
• Date: 2025-12-02
• Authors: Jicheng Wang, Hongliang Wang, Xiaoting Guo, Yingjie Li, Z. H. He
• DOI: 10.1016/j.jhydrol.2025.134710

Research Groups

• Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan, Shanxi, China
• State key Laboratory of Extreme Environment Optoelectronic Dynamic Measurement Technology and Instrument, North University of China, Taiyuan, Shanxi, China
• Department of Computer and Information Engineering, Shanxi Institute of Energy, Taiyuan, Shanxi, China

Short Summary

This study introduces an automatic optimal search line selection algorithm to enhance the accuracy of Space-Time Image Velocimetry (STIV) for river surface flow velocity measurement. The proposed method significantly reduces errors in velocity estimation compared to traditional fixed-line STIV, improving its robustness and applicability across various flow conditions.

Objective

• To address the limitations of traditional Space-Time Image Velocimetry (STIV) methods, which suffer from low-quality space-time images (STIs) and reduced velocity estimation precision due to fixed, evenly spaced search lines failing to capture distinct surface textures.
• To develop and validate an automatic optimal search line selection algorithm based on quantitative STI quality evaluation to improve the accuracy of main orientation of texture (MOT) extraction and surface flow velocity measurement.

Study Configuration

• Spatial Scale: Field experiments were conducted in diverse environments including artificial channels, natural rivers, and urban flood scenarios, covering a range of flow complexities.
• Temporal Scale: The method is designed for real-time or near real-time measurement of surface flow velocity, with validation involving video data capture and analysis.

Methodology and Data

• Models used: Space-Time Image Velocimetry (STIV) coupled with a novel automatic optimal search line selection algorithm. The algorithm utilizes a set of quantitative STI quality indicators to identify optimal search lines.
• Data sources: Video-based, non-contact measurements acquired during field experiments in various hydrological settings.

Main Results

• The proposed automatic optimal search line algorithm effectively selects high-quality search lines within sub-regions based on quantitative STI quality indicators.
• Compared to the traditional fixed-line STIV method, the new algorithm significantly reduces errors in both main orientation of texture (MOT) detection and surface flow velocity estimation.
• The enhanced STIV method demonstrates greater adaptability and robustness, thereby improving its applicability and measurement accuracy under complex flow conditions.

Contributions

• Introduction of an automatic search line optimization algorithm that improves STIV performance by selecting optimal lines based on quantitative STI quality indicators.
• Comprehensive field validation across diverse scenarios, including artificial channels, natural rivers, and urban flood events, demonstrating the method's practical applicability and superior performance.
• Significant reduction in MOT detection and velocity estimation errors, enhancing the overall accuracy and robustness of non-contact surface flow velocity measurements.

Funding

• No funding information was provided in the article text.

Citation

@article{Wang2025Improving,
  author = {Wang, Jicheng and Wang, Hongliang and Guo, Xiaoting and Li, Yingjie and He, Z. H.},
  title = {Improving river surface flow velocity measurement by coupling optimal search line algorithm with space-time image velocimetry},
  journal = {Journal of Hydrology},
  year = {2025},
  doi = {10.1016/j.jhydrol.2025.134710},
  url = {https://doi.org/10.1016/j.jhydrol.2025.134710}
}