Peer-reviewed veterinary case report
How machine learning predicts trawl net fluttering behavior
By Liu W et al.·2025·East China Sea Fisheries Research Institute, China·View original on Europe PMC →
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Original publication title: Estimating dynamic behavior of trawl codend based on machine learning models.
- Species:
- fish
Plain-English summary
This study looked at how different factors affect the movement of trawl codends, which are the nets used in fishing, to help improve how fish are caught while allowing unwanted fish to escape. Researchers tested things like the size of the mesh, the thickness of the twine, and the length of the codend in a controlled water tank. They found that empty codends had a certain shape that was different from those with fish in them, and that some types of mesh held their shape better than others. The study used a type of computer model to predict how these nets would behave in different situations, and the predictions were very accurate. Overall, the findings can help make fishing more efficient and reduce bycatch, which is when unwanted fish are caught.
Abstract
Understanding the hydrodynamic behaviors and fluttering motions of trawl codends is crucial for improving trawl selectivity and controlling fish escape while retaining desired catch. This study investigated factors like mesh size, twine diameter, codend length, knot direction, and twine material on codend fluttering in a flume tank. Fast Fourier Transform (FFT) visualized the Strouhal number, fluttering amplitude, and drag force evolution. Backpropagation (BP) neural networks were used to predict oscillatory characteristics under various conditions. Results showed that empty codends had a waist-to-end hanging ratio > 0.8, while codends with catch had ratios < 1.2. T<sub>90</sub> and polyethylene meshes maintained mesh openings better than T<sub>0</sub> and nylon. The mean drag force coefficient decreased with Reynolds number and then tended to level off slowly. Extreme fluttering amplitudes occurred at Strouhal numbers between 5.41 × 10⁻⁴ and 7.15 × 10⁻⁴. Drag force amplitude increased with mean drag force coefficient for empty codends, but for codends with catch, it increased with smaller mesh sizes, twine diameters, and higher flow velocities. Positional amplitude increased with lower waist-to-end hanging ratio. BP neural network predictions matched experimental results with over 90% accuracy, demonstrating its effectiveness in predicting codend oscillations under varying parameters.
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Search related cases →Original publication on Europe PMC: https://europepmc.org/article/MED/41360866