Fishing Effort Estimation of Trawlers Based on Vessel Monitoring System Data  

基于船舶监测系统数据的拖网渔船捕捞努力量估算

作　　者：LI Dan LU Feng XU Shuo WANG Yu XUE Muhan NI Hanchen FANG Hui ZHANG Man MA Zhenhua CHEN Zuozhi XU Jian 李丹;鲁峰;徐硕;王宇;薛沐涵;倪翰晨;方辉;张漫;马振华;陈作志;许建(中国水产科学研究院渔业工程研究所,北京100141;崂山实验室,青岛266237;中国水产科学研究院东海水产研究所,上海200090;中国农业大学信息与电气工程学院,北京100083;中国水产科学研究院南海水产研究所,广州510300)

机构地区：[1]Institute of Fisheries Engineering,Chinese Academy of Fishery Sciences,Beijing 100141,China [2]Laoshan Laboratory,Qingdao 266237,China [3]East China Sea Fisheries Research Institute,Chinese Academy of Fishery Sciences,Shanghai 200090,China [4]College of Information and Electrical Engineering,China Agricultural University,Beijing 100083,China [5]South China Sea Fisheries Research Institute,Chinese Academy of Fishery Sciences,Guangzhou 510300,China

出　　处：《农业机械学报》2025年第2期523-532,共10页Transactions of the Chinese Society for Agricultural Machinery

基　　金：中国水产科学研究院中央级公益性科研院所基本科研业务费专项(2024HY-ZC006、2023TD91);国家重点研发计划项目(2024YFD2400505、2024YFD2400801);崂山实验室科技创新项目(LSKJ202203003)。

摘　　要：Estimating trawler fishing effort plays a critical role in characterizing marine fisheries activities,quantifying the ecological impact of trawling,and refining regulatory frameworks and policies.Understanding trawler fishing inputs offers crucial scientific data to support the sustainable management of offshore fishery resources in China.An XGBoost algorithm was introduced and optimized through Harris Hawks Optimization(HHO),to develop a model for identifying trawler fishing behaviour.The model demonstrated exceptional performance,achieving accuracy,sensitivity,specificity,and the Matthews correlation coefficient of 0.9713,0.9806,0.9632,and 0.9425,respectively.Using this model to detect fishing activities,the fishing effort of trawlers from Shandong Province in the sea area between 119°E to 124°E and 32°N to 40°N in 2021 was quantified.A heatmap depicting fishing effort,generated with a spatial resolution of 1/8°,revealed that fishing activities were predominantly concentrated in two regions:121.1°E to 124°E,35.7°N to 38.7°N,and 119.8°E to 122.8°E,33.6°N to 35.4°N.This research can provide a foundation for quantitative evaluations of fishery resources,which can offer vital data to promote the sustainable development of marine capture fisheries.拖网渔船捕捞努力量的估算对于描述海洋渔业活动、量化拖网作业对海洋造成的生态压力以及修订渔业法规和政策具有重要意义。明确拖网渔船的捕捞投入可为中国近海渔业资源的可持续发展提供科学数据支持。本研究提出了一种基于Harris Hawks Optimization(HHO)优化的XGBoost算法,用于构建拖网渔船捕捞行为识别模型。结果表明,该模型准确率、灵敏度、特异度和马修斯相关系数分别为0.9713、0.9806、0.9632和0.9425。利用该模型识别拖网渔船的捕捞行为并计算了2021年在119°E~124°E、32°N~40°N海域内山东省拖网渔船的捕捞努力量。以空间精度1/8°生成了捕捞努力量热力图,计算结果揭示了捕捞活动的空间分布主要集中在2个关键区域:121.1°E~124°E、35.7°N~38.7°N和119.8°E~122.8°E、33.6°N~35.4°N。本研究可为渔业资源的定量评估奠定基础,为海洋捕捞渔业的可持续发展提供必要数据。

关 键 词：TRAWLER vessel position data machine learning fishing effort 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]