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

• Published in: Marine pollution bulletin 2023-08, Vol.193, p.115158-115158, Article 115158
• Main Authors: Han, Haibin, Yang, Chao, Jiang, Bohui, Shang, Chen, Sun, Yuyan, Zhao, Xinye, Xiang, Delong, Zhang, Heng, Shi, Yongchuang
• Format: Article
• Language: English
• Subjects: Convolutional neural networks; Fishing ground prediction; Multi-factor ocean remote-sensing environmental changes; Northwest Pacific Ocean; Scomber japonicus; Spatiotemporal information
• ISSN: 0025-326X; 1879-3363
• DOI: 10.1016/j.marpolbul.2023.115158

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°∼43°N, 149°∼154°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. [Display omitted] •Predicting chub mackerel fishery is important for food security, cost-saving, and sustainable development.•Gravity center and statistical analysis used to study chub mackerel fishery variability.•First demonstration of the importance of spatial and temporal scales for predicting fisheries using a deep learning model.•2DCNN and 3DCNN models applied to predict chub mackerel fishery in the high seas.•Revealed key technology for predicting fishing grounds using marine remote sensing images.