Clean fishing: Construction of prediction model for high-catch Antarctic krill (Euphausia superba) fishing grounds based on deep learning and dynamic sliding window methods
Achieving energy-efficient, precise, and overall efficient production of Antarctic krill (Euphausia superba) is critical for realizing sustainable and ecological fisheries in the context of ongoing natural and anthropogenic climate change. In this study, we comprehensively analyzed commercial E. sup...
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Elsevier
2025-05-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125000561 |
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| author | Haibin Han Bohui Jiang Hongliang Huang Yang Li Jianghua Sui Guoqing Zhao Yuhan Wang Heng Zhang Shenglong Yang Yongchuang Shi |
| author_facet | Haibin Han Bohui Jiang Hongliang Huang Yang Li Jianghua Sui Guoqing Zhao Yuhan Wang Heng Zhang Shenglong Yang Yongchuang Shi |
| author_sort | Haibin Han |
| collection | DOAJ |
| description | Achieving energy-efficient, precise, and overall efficient production of Antarctic krill (Euphausia superba) is critical for realizing sustainable and ecological fisheries in the context of ongoing natural and anthropogenic climate change. In this study, we comprehensively analyzed commercial E. superba statistics and multivariate marine environmental data from 2010 to 2022 using the gravity center of the fishing ground method, dynamic sliding window, 3DCNN, and 3DCNN-ConvLSTM models. Results: 1) Inter-annual and inter-weekly catch varied significantly, with the total weekly catch evenly distributed between 0 and 2600 tons. The annual gravity center of the fishing grounds varied considerably between years and was mainly concentrated around the islands and in the strait. 2) Neither long- nor short-time-series historical data led to the best prediction. The optimal sliding window size for the 3DCNN was 4, whereas it was 11 for the 3DCNN-ConvLSTM model. 3) Climate change must be considered when selecting data, and the addition of biased data may negatively affect the model's predictive performance. 4) When using an optimal sliding window, the 3DCNN model outperformed the 3DCNN-ConvLSTM model. 5) The 3DCNN model tends to learn information about the environmental variables with the most significant differences in different categories of fishing grounds. This study aids in efficient selection of the most relevant historical data and an optimal model for developing a prediction model for high-catch fishing grounds, thereby providing a scientific foundation for clean production, sustainable development, and effective management of the E. superba fishery. |
| format | Article |
| id | doaj-art-64ba71c3328e44d69b0ee4d7690f248f |
| institution | OA Journals |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-64ba71c3328e44d69b0ee4d7690f248f2025-08-20T02:04:00ZengElsevierEcological Informatics1574-95412025-05-018610304710.1016/j.ecoinf.2025.103047Clean fishing: Construction of prediction model for high-catch Antarctic krill (Euphausia superba) fishing grounds based on deep learning and dynamic sliding window methodsHaibin Han0Bohui Jiang1Hongliang Huang2Yang Li3Jianghua Sui4Guoqing Zhao5Yuhan Wang6Heng Zhang7Shenglong Yang8Yongchuang Shi9Key Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, P.R.China, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China; College of Marine Living Resource Sciences and Management, Shanghai Ocean University, Shanghai, ChinaKey Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, P.R.China, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China; College of Marine Living Resource Sciences and Management, Shanghai Ocean University, Shanghai, ChinaKey Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, P.R.China, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, ChinaKey Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, P.R.China, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China; College of Navigation and Ship Engineering, Dalian Ocean University, Dalian, ChinaCollege of Navigation and Ship Engineering, Dalian Ocean University, Dalian, ChinaKey Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, P.R.China, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China; Key Laboratory of Polar Ecosystem and Climate Change (Shanghai Jiao Tong University), Ministry of Education, 1954 Huashan Road, Shanghai 200030, ChinaCollege of Navigation and Ship Engineering, Dalian Ocean University, Dalian, ChinaKey Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, P.R.China, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China; Corresponding authors.Key Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, P.R.China, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China; Corresponding authors.Key Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, P.R.China, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China; Corresponding authors.Achieving energy-efficient, precise, and overall efficient production of Antarctic krill (Euphausia superba) is critical for realizing sustainable and ecological fisheries in the context of ongoing natural and anthropogenic climate change. In this study, we comprehensively analyzed commercial E. superba statistics and multivariate marine environmental data from 2010 to 2022 using the gravity center of the fishing ground method, dynamic sliding window, 3DCNN, and 3DCNN-ConvLSTM models. Results: 1) Inter-annual and inter-weekly catch varied significantly, with the total weekly catch evenly distributed between 0 and 2600 tons. The annual gravity center of the fishing grounds varied considerably between years and was mainly concentrated around the islands and in the strait. 2) Neither long- nor short-time-series historical data led to the best prediction. The optimal sliding window size for the 3DCNN was 4, whereas it was 11 for the 3DCNN-ConvLSTM model. 3) Climate change must be considered when selecting data, and the addition of biased data may negatively affect the model's predictive performance. 4) When using an optimal sliding window, the 3DCNN model outperformed the 3DCNN-ConvLSTM model. 5) The 3DCNN model tends to learn information about the environmental variables with the most significant differences in different categories of fishing grounds. This study aids in efficient selection of the most relevant historical data and an optimal model for developing a prediction model for high-catch fishing grounds, thereby providing a scientific foundation for clean production, sustainable development, and effective management of the E. superba fishery.http://www.sciencedirect.com/science/article/pii/S1574954125000561Euphausia superbaDeep learningDynamic sliding windowFishing grounds predictionPolar fishery |
| spellingShingle | Haibin Han Bohui Jiang Hongliang Huang Yang Li Jianghua Sui Guoqing Zhao Yuhan Wang Heng Zhang Shenglong Yang Yongchuang Shi Clean fishing: Construction of prediction model for high-catch Antarctic krill (Euphausia superba) fishing grounds based on deep learning and dynamic sliding window methods Ecological Informatics Euphausia superba Deep learning Dynamic sliding window Fishing grounds prediction Polar fishery |
| title | Clean fishing: Construction of prediction model for high-catch Antarctic krill (Euphausia superba) fishing grounds based on deep learning and dynamic sliding window methods |
| title_full | Clean fishing: Construction of prediction model for high-catch Antarctic krill (Euphausia superba) fishing grounds based on deep learning and dynamic sliding window methods |
| title_fullStr | Clean fishing: Construction of prediction model for high-catch Antarctic krill (Euphausia superba) fishing grounds based on deep learning and dynamic sliding window methods |
| title_full_unstemmed | Clean fishing: Construction of prediction model for high-catch Antarctic krill (Euphausia superba) fishing grounds based on deep learning and dynamic sliding window methods |
| title_short | Clean fishing: Construction of prediction model for high-catch Antarctic krill (Euphausia superba) fishing grounds based on deep learning and dynamic sliding window methods |
| title_sort | clean fishing construction of prediction model for high catch antarctic krill euphausia superba fishing grounds based on deep learning and dynamic sliding window methods |
| topic | Euphausia superba Deep learning Dynamic sliding window Fishing grounds prediction Polar fishery |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125000561 |
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