上海海洋大学学报2026，Vol.35Issue(3)：767-781,15.DOI:10.12024/jsou.20250404850
基于深度学习的东南太平洋茎柔鱼渔场预测
Deep learning-based fishing ground prediction of Dosidicus gigas in the Southeast Pacific
摘要
Abstract
To improve the spatiotemporal granularity of fishing-ground prediction for Dosidicusgigasin the southeastern Pacific,we evaluated how multiple time scales and environmental-factor combinations influence the distribution of the center fishing ground.Using fishery and environmental datasets from 2012 to 2021,we considered five temporal windows(3 d,6 d,10 d,15 d,30 d)and seven input combinations derived from sea surface temperature(SST),sea surface height(SSH),sea surface salinity(SSS),and photosynthetically active radiation(Par).The application effect index of fishing ground(AEIFG)was introduced to define the center fishing ground as the prediction target.Because PAR degraded performance at the 30 d scale,we applied a weight-optimization scheme to mitigate its adverse effect.We compared three models:U-Net,generalized additive model(GAM),and artificial neural network(ANN).Results show that U-Net consistently achieved the best performance across schemes and accurately captured the spatial structure of center fishing grounds.Prediction accuracy depended strongly on the environmental-factor configuration,with the SST and SSS combination performing best overall.These findings demonstrate that deep learning can effectively integrate multi-source oceanographic information to enhance prediction accuracy for fishing grounds,providing methodological support for multi-timescale forecasting and intelligent fishery information services.关键词
茎柔鱼/渔场预测/多环境因子/中心渔场定义/深度学习/U-Net模型/东南太平洋Key words
Dosidicus gigas/fishing ground prediction/multiple environmental factors/central fishing ground definition/deeplearning/U-Net/Southeast Pacific分类
农业科技引用本文复制引用
徐佳雯,解明阳,柳彬,余为,陈新军,汪金涛,王晓辉..基于深度学习的东南太平洋茎柔鱼渔场预测[J].上海海洋大学学报,2026,35(3):767-781,15.基金项目
上海市教育委员会AI专项(A1-3405-25-000303) （A1-3405-25-000303）
国家自然科学基金(42476086,42006159) （42476086,42006159）
国家重点研发计划(2023YFD2401305) （2023YFD2401305）
华为AI百校计划(hid09976227) （hid09976227）
鱿鱼渔业渔情预报服务系统开发项目(COFC-C-S-2024-003) （COFC-C-S-2024-003）
