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<p>Forecasting short-term fishing effort distribution is crucial for fishery management to promote sustainable development and dynamically protect the ecosystem. Utilization of impact factors, such as marine hydrological factors and chlorophyll concentration distribution, can aid in short-term forecasting. However, two significant challenges emerge: firstly, the relationship between these impact factors and fishing effort distributions require comprehensive analysis; secondly, the forecasting model must effectively integrate all spatio-temporal features derived from these factors. In addressing these challenges, this study commences with a quantitative analysis of the relationships between these impact factors and fishing effort distributions. Subsequently, we introduce TransFish, a deep learning approach that forecasts day-level fishing effort distribution by harnessing these impact factors. TransFish integrates ResNet and Transformer network, seamlessly synthesizing spatial features from historical fishing effort distributions, marine hydrological factor fields, and chlorophyll concentration distributions, while also accounting for temporal relations within forecasting sequences. The performance of TransFish is evaluated using the Vessel Monitoring System dataset, which contains spatial and temporal fishing effort information from 1589 trawlers in the East China Sea, spanning September 2015 to May 2017. Additionally, ocean biogeochemical factors, such as sea surface temperature and chlorophyll concentration, are used as environmental forecasting variables. The dataset from September 2015 to May 2016 is utilized for relationship analysis and model training, while the dataset from September 2016 to May 2017 is employed to evaluate forecasting accuracy. The results reveal that the daily forecasting error ratio for the subsequent week ranges from 4.51 to 6.01%, with an average error ratio of 5.32% across all weeks for the test dataset, thereby confirming TransFish’s effectiveness and reliability.</p>

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TransFish: day-level forecasting of fishing effort distribution via transformer on multi-source data

  • 期刊论文

  • 发表日期：

    2025年6月7日

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  • Zhongning Zhao,
  • Feng Hong,
  • Yuqi Shi,
  • Yufei Jiang,
  • Haiguang Huang,
  • Guiyuan Jiang,
  • Zhongwen Guo

摘要

Forecasting short-term fishing effort distribution is crucial for fishery management to promote sustainable development and dynamically protect the ecosystem. Utilization of impact factors, such as marine hydrological factors and chlorophyll concentration distribution, can aid in short-term forecasting. However, two significant challenges emerge: firstly, the relationship between these impact factors and fishing effort distributions require comprehensive analysis; secondly, the forecasting model must effectively integrate all spatio-temporal features derived from these factors. In addressing these challenges, this study commences with a quantitative analysis of the relationships between these impact factors and fishing effort distributions. Subsequently, we introduce TransFish, a deep learning approach that forecasts day-level fishing effort distribution by harnessing these impact factors. TransFish integrates ResNet and Transformer network, seamlessly synthesizing spatial features from historical fishing effort distributions, marine hydrological factor fields, and chlorophyll concentration distributions, while also accounting for temporal relations within forecasting sequences. The performance of TransFish is evaluated using the Vessel Monitoring System dataset, which contains spatial and temporal fishing effort information from 1589 trawlers in the East China Sea, spanning September 2015 to May 2017. Additionally, ocean biogeochemical factors, such as sea surface temperature and chlorophyll concentration, are used as environmental forecasting variables. The dataset from September 2015 to May 2016 is utilized for relationship analysis and model training, while the dataset from September 2016 to May 2017 is employed to evaluate forecasting accuracy. The results reveal that the daily forecasting error ratio for the subsequent week ranges from 4.51 to 6.01%, with an average error ratio of 5.32% across all weeks for the test dataset, thereby confirming TransFish’s effectiveness and reliability.