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Deep learning-based fishing ground prediction with multiple environmental factors

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Abstract

Improving the accuracy of fishing ground prediction for oceanic economic species has always been one of the most concerning issues in fisheries research. Recent studies have confirmed that deep learning has achieved superior results over traditional methods in the era of big data. However, the deep learning-based fishing ground prediction model with a single environment suffers from the problem that the area of the fishing ground is too large and not concentrated. In this study, we developed a deep learning-based fishing ground prediction model with multiple environmental factors using neon flying squid (Ommastrephes bartramii) in Northwest Pacific Ocean as an example. Based on the modified U-Net model, the approach involves the sea surface temperature, sea surface height, sea surface salinity, and chlorophyll a as inputs, and the center fishing ground as the output. The model is trained with data from July to November in 2002–2019, and tested with data of 2020. We considered and compared five temporal scales (3, 6, 10, 15, and 30 days) and seven multiple environmental factor combinations. By comparing different cases, we found that the optimal temporal scale is 30 days, and the optimal multiple environmental factor combination contained SST and Chl a. The inclusion of multiple factors in the model greatly improved the concentration of the center fishing ground. The selection of a suitable combination of multiple environmental factors is beneficial to the precise spatial distribution of fishing grounds. This study deepens the understanding of the mechanism of environmental field influence on fishing grounds from the perspective of artificial intelligence and fishery science.

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Data availability

The fishery data are not available for sharing at the request of the copyright holder. The environmental factor data used in this study are available from OceanWatch of the National Oceanic and Atmospheric Administration and the University of Hawaii. Users can download these data from online services (https://oceanwatch.pifsc.noaa.gov/erddap/griddap/CRW_sst_v1_0.html; http://apdrc.soest.hawaii.edu/data) for free.

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Acknowledgements

The work is supported in part by the National Natural Science Foundation of China under Grant 41876141 and Grant 42006159, and in part by the National Key R&D Programme of China under Grant 2019YFD0901404. The authors thank the Chinese Squid-Jigging Technology Group at Shanghai Ocean University for providing the fishery data. The authors thank the National Oceanic and Atmospheric Administration and the University of Hawaii for providing the environmental data.

Author information

Authors and Affiliations

  1. College of Marine Sciences, Shanghai Ocean University, Shanghai, 201306, China

    Mingyang Xie, Bin Liu & Xinjun Chen

  2. Key Laboratory of Marine Ecological Monitoring and Restoration Technologies, Ministry of Natural Resources, Shanghai, 200137, China

    Bin Liu

  3. Key Laboratory of Oceanic Fisheries Exploration, Ministry of Agriculture and Rural Affairs, Shanghai Ocean University, Shanghai, 201306, China

    Xinjun Chen

  4. National Engineering Research Center for Oceanic Fisheries, Shanghai Ocean University, Shanghai, 201306, China

    Xinjun Chen

  5. Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai Ocean University, Shanghai, 201306, China

    Xinjun Chen

Authors
  1. Mingyang Xie
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  2. Bin Liu
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Contributions

XJC and BL conceived the idea. MYX carried out the experiments and wrote the manuscript. BL and XJC revised the manuscript. All authors contributed to the article and approved the submitted version.

Corresponding authors

Correspondence to Bin Liu or Xinjun Chen.

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Conflict of interest

We, i.e., all the authors, have no conflicts of interest to disclose.

Animal and human rights statement

No human or animal subjects were used during the course of this research.

Additional information

Edited by Xin Yu.

Special Topic: Fishery Science & Technology.

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Xie, M., Liu, B. & Chen, X. Deep learning-based fishing ground prediction with multiple environmental factors. Mar Life Sci Technol (2024). https://doi.org/10.1007/s42995-024-00222-4

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