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Deep learning-based fishing ground prediction with multiple environmental factors.

Mingyang Xie1, Bin Liu1,2, Xinjun Chen1,3,4,5

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

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|December 2, 2024
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This study introduces a deep learning model for predicting fishing grounds, improving accuracy by using multiple environmental factors like sea surface temperature and chlorophyll a. The enhanced model provides more concentrated and precise fishing ground predictions for oceanic species.

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Center fishing groundDeep learningMultiple environmental factorsOmmastrephes bartramiiTemporal scalesU-Net

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Area of Science:

  • Fisheries Science
  • Artificial Intelligence
  • Oceanography

Background:

  • Accurate fishing ground prediction is crucial for fisheries research.
  • Deep learning models outperform traditional methods in big data scenarios.
  • Single-environment deep learning models yield imprecise, large fishing areas.

Purpose of the Study:

  • To develop an improved deep learning model for precise fishing ground prediction.
  • To identify optimal environmental factors and temporal scales for prediction accuracy.
  • To enhance the concentration and spatial distribution of predicted fishing grounds.

Main Methods:

  • Utilized a modified U-Net deep learning model.
  • Incorporated multiple environmental factors: sea surface temperature (SST), sea surface height, sea surface salinity, and chlorophyll a (Chl a).
  • Trained and tested the model using data from the Northwest Pacific Ocean for neon flying squid (Ommastrephes bartramii).

Main Results:

  • The optimal temporal scale for prediction was found to be 30 days.
  • The most effective combination of environmental factors included SST and Chl a.
  • Integrating multiple environmental factors significantly improved the concentration of the predicted center fishing ground.

Conclusions:

  • A multi-factor deep learning approach enhances fishing ground prediction accuracy and spatial precision.
  • Optimal environmental factor combinations and temporal scales are key to precise fishing ground identification.
  • This research advances understanding of environmental influences on fishing grounds through AI and fisheries science.