海洋科学2023，Vol.47Issue(9)：61-70,10.DOI:10.11759/hykx20221127002
机器学习方法在舟山渔场主要经济蟹类生物量估算中的应用
Application of machine learning methods for estimating the biomass of economically important crabs in the Zhoushan fishery
摘要
Abstract
The swept area method is currently widely used in biomass assessment of fisheries because of its sim-plicity.However,this method assumes a uniform distribution of resources,and to improve the accuracy of biomass assessment,many stations must be sampled,which increases financial costs.In this study,we simulated and ana-lyzed the biomass assessment process;further,we explored the use of machine learning methods to assess the bio-mass of economically important crab species Portunus trituberculatus,Charybdis bimaculata,Charybdis japonica,and Ovalipes punctatus in the Zhoushan fishing ground based on data obtained from bottom trawl surveys of fishery resources conducted in August 2006 and January,May,and November 2007.The results showed that with the re-duction of the number of survey stations,the performance of the Extreme Gradient Boost method was better than that of the swept area method in autumn and winter when crabs were dispersed,and the estimated error decreased by 7.49%-21.34%.In spring and summer,when crabs were more evenly dispersed,there was no significant differ-ence between the estimated biomass obtained using the swept area method and machine learning methods(P＜0.05).We conclude that the machine learning methods improve the accuracy of assessment and save the cost of resource surveys,suggesting that they can be used in the biomass assessment of other fishery resource species.关键词
资源评估/扫海面积法/随机森林/梯度提升回归树/极限梯度提升回归Key words
stock assessment/swept area method/random forest/gradient lifting regression tree/extreme gradient boosting分类
农业科技引用本文复制引用
杨春蕙,栗小东,刘琦,王迎宾..机器学习方法在舟山渔场主要经济蟹类生物量估算中的应用[J].海洋科学,2023,47(9):61-70,10.基金项目
浙江省基础公益计划项目(LGN21C190009) （LGN21C190009）
舟山市科技局项目(2022C41003)Zhejiang Basic Public Welfare Project,No.LGN21C190009 （2022C41003）
Zhoushan Science and Technology Bureau Project,No.2022C41003 （）
