渔业信息与战略2025，Vol.40Issue(3)：184-194,11.DOI:10.13233/j.cnki.fishis.2025.02.004
深度学习技术在渔情预报中的应用研究
Research on application of deep learning techniques in fishery forecasting
摘要
Abstract
Fishery forecasting is a technology that utilizes multi-source data and computational models to predict and disseminate information on the spatial distribution,density,and dynamic patterns of fishery resources.In recent years,the continuous advancement of deep learning algorithms has provided more accurate technical approaches for fishery forecasting.This paper systematically reviews the application of deep learning techniques in marine fishery forecasting,with a specific analysis of marine environmental factors such as sea surface temperature(SST),sea surface height(SSH),sea surface salinity(SSS),and chlorophyll-a(Chla)concentration,as well as fishery prediction indicators including fishing effort and catch per unit effort(CPUE).Building on this foundation,the research progress of deep learning in fishery forecasting is summarized,its application prospects in fisheries management are explored,and current challenges in data processing,model selection and optimization,real-time performance,and responsiveness are identified.Future research directions are also proposed.To enhance the practicality and accuracy of fishery forecasting,it is recommended to integrate multi-modal data fusion,transfer learning,reinforcement learning,and intelligent technologies.These advancements are expected to provide robust support for sustainable fisheries management.关键词
渔情预报/渔业管理/深度学习/海洋环境Key words
fishery forecasting/fishery management/deep learning/marine environment分类
农业科技引用本文复制引用
张蕾蕾,石永闯,杨胜龙,张胜茂..深度学习技术在渔情预报中的应用研究[J].渔业信息与战略,2025,40(3):184-194,11.基金项目
崂山实验室科技创新项目(LSKJ202201804) （LSKJ202201804）
