Construction of chub mackerel (Scomber japonicus) fishing ground prediction model in the northwestern Pacific Ocean based on deep learning and marine environmental variables

文献类型： 外文期刊

第一作者： Han, Haibin

作者： Han, Haibin;Yang, Chao;Jiang, Bohui;Shang, Chen;Sun, Yuyan;Xiang, Delong;Zhang, Heng;Shi, Yongchuang;Han, Haibin;Yang, Chao;Jiang, Bohui;Shang, Chen;Sun, Yuyan;Xiang, Delong;Zhang, Heng;Shi, Yongchuang;Han, Haibin;Yang, Chao;Jiang, Bohui;Shang, Chen;Sun, Yuyan;Zhao, Xinye;Xiang, Delong

作者机构：

关键词： Northwest Pacific Ocean; Scomber japonicus; Spatiotemporal information; Convolutional neural networks; Fishing ground prediction; Multi-factor ocean remote-sensing environmental changes

期刊名称：MARINE POLLUTION BULLETIN （ 影响因子：5.8； 五年影响因子：6.5 ）

ISSN： 0025-326X

年卷期： 2023 年 193 卷

页码：

收录情况： SCI

摘要： Accurate prediction of the central fishing grounds of chub mackerel is substantial for assessing and managing marine fishery resources. Based on the high-seas chub mackerel fishery statistics and multi-factor ocean remote-sensing environmental data in the Northwest Pacific Ocean from 2014 to 2021, this article applied the gravity center of the fishing grounds, 2DCNN, and 3DCNN models to analyze the spatial and temporal variability of the chub mackerel catches and fishing grounds. Results:1) the primary fishing season of chub mackerel fishery was April-November which catches were mainly concentrated in 39 degrees similar to 43 degrees N, 149 degrees similar to 154 degrees E. 2) Since 2019, the annual gravity center of the fishing grounds has continued to move northeastward; the monthly gravity center has prominent seasonal migratory characteristics. 3) 3DCNN model was better than the 2DCNN model. 4) For 3DCNN, the model prioritized learning information on the most easily distinguishable ocean remote-sensing environmental variables in different classifications.

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