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Clean fishing: Construction of prediction model for high-catch Antarctic krill (Euphausia superba) fishing grounds based on deep learning and dynamic sliding window methods

文献类型： 外文期刊

作者： Han, Haibin 1 ; Jiang, Bohui 1 ; Huang, Hongliang 1 ; Li, Yang 1 ; Sui, Jianghua 3 ; Zhao, Guoqing 1 ; Wang, Yuhan 3 ; Zhang, Heng 1 ; Yang, Shenglong 1 ; Shi, Yongchuang 1 ;

作者机构： 1.Chinese Acad Fishery Sci, Key Lab Ocean & Polar Fisheries, Minist Agr & Rural Affairs, East China Sea Fisheries Res Inst, Shanghai 200090, Peoples R China

2.Shanghai Ocean Univ, Coll Marine Living Resource Sci & Management, Shanghai, Peoples R China

3.Dalian Ocean Univ, Coll Nav & Ship Engn, Dalian, Peoples R China

4.Shanghai Jiao Tong Univ, Key Lab Polar Ecosyst & Climate Change, Minist Educ, 1954 Huashan Rd, Shanghai 200030, Peoples R China

关键词： Euphausia superba; Deep learning; Dynamic sliding window; Fishing grounds prediction; Polar fishery

期刊名称：ECOLOGICAL INFORMATICS （ 影响因子：7.3； 五年影响因子：7.1 ）

ISSN： 1574-9541

年卷期： 2025 年 86 卷

页码：

收录情况： SCI

摘要： 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.

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