基于集成学习的大西洋热带水域大眼金枪鱼渔情预报  

Fishing ground forecasting of bigeye tuna(Thunnus obesus)in the tropical waters of Atlantic Ocean based on ensemble learning

作　　者：宋利明[1,2] 任士雨 张敏[1,2] 隋恒寿[3] SONG Liming;REN Shiyu;ZHANG Min;SUI Hengshou(College of Marine Sciences,Shanghai Ocean University,Shanghai 201306,China;National Engineering Research Center for Oceanic Fisheries,Shanghai Ocean University,Shanghai 201306,China;CNFC Overseas Fisheries Co.,LTD.,Beijing 100032,China)

机构地区：[1]上海海洋大学海洋科学学院,上海201306 [2]上海海洋大学,国家远洋渔业工程技术研究中心,上海201306 [3]中水集团远洋股份有限公司,北京100032

出　　处：《水产学报》2023年第4期62-74,共13页Journal of Fisheries of China

基　　金：国家重点研发计划(2020YFD0901205);2016年农业农村部海洋渔业资源调查与探捕项目(D-8006-16-8045)。

摘　　要：为提高大西洋大眼金枪鱼渔场预报模型的准确率,实验利用13艘中国延绳钓渔船2013—2019年的渔捞日志数据和对应的海洋环境数据(海表面风速、叶绿素a浓度、涡动能、混合层深度和0~500 m水层的垂直温度、盐度和溶解氧等),以天为时间分辨率、2°×2°为空间分辨率、以数据集的75%为训练数据建立了K最近邻(KNN)、逻辑斯蒂回归(LR)、分类与回归树(CART)、支持向量机(SVM)、人工神经网络(ANN)、随机森林(RF)、梯度提升决策树(GBDT)和Stacking集成(STK)渔情预报模型,以25%的测试数据进行模型性能测试、比较。结果显示,(1)STK(由KNN、RF、GBDT模型集成)模型的大眼金枪鱼渔场预报性能较KNN、LR、CART、SVM、ANN、RF和GBDT模型有所提高且相对稳定,上述模型对应的准确率和ROC曲线下面积(AUC)依次分别为81.62%、0.781,79.44%、0.778,72.81%、0.685,74.84%、0.717,73.67%、0.702,67.70%、0.500,80.96%、0.780和78.13%、0.747;(2)STK模型预测的中心渔场与实际中心渔场基本吻合,主要在5°N~10°N,33°W~43°W海域附近;(3)影响大西洋大眼金枪鱼渔场分布的海洋环境因子主要有300 m水层的溶解氧、500 m水层的盐度、海面风速和混合层深度,相对重要性分别为13.24%、9.12%、9.12%和8.81%。研究表明,STK模型对大西洋大眼金枪鱼渔场的预报准确率较高。In order to improve the accuracy of bigeye tuna(Thunnus obesus)fishing ground forecast model in the tropical waters of Atlantic Ocean,a series of fishery forecast models were established based on the logbook data of 13 Chinese longliners from 2013 to 2019 and the corresponding marine environment data,e.g.sea surface wind speed,chlorophyll a concentration,eddy kinetic energy,upper boundary depth of thermocline,vertical temperature,salinity and dissolved oxygen in 0-500 m water layer.T.obesus CPUE was calculated based on the logbook data.The environmental factors related to T.obesus CPUE were screened out from 29 environmental factors by correlation analysis.The non-collinear environmental factors were selected by collinearity analysis based on the variance expansion factor(VIF)and used to build the bigeye tuna fishing ground prediction models.The Spearman correlation coefficients between non-collinear environmental factors and T.obesus CPUE were calculated and used to analyze the relative importance of the environmental factors to the T.obesus CPUE.These series of prediction models,e g.K-Nearest Neighbor(KNN),Logistic Regression(LR),Classification and Regression Tree(CART),Support Vector Machine(SVM),Artificial Neural Networks(ANN),Random Forest(RF),Gradient Boosting Decision Tree(GBDT),and Stacking ensemble model(developed by KNN,RF and GBDT,STK)were built by using 75% of data and verified by using 25% of data.The time resolution of T.obesus CPUE and marine environment data was one day,and the spatial resolution was 2°×2°.The performance of 8 models were evaluated by the area under the receiver operating characteristic curve(AUC)and prediction accuracy.The maps of the actual fishing ground and the predicted fishing ground were overlapped by ArcGIS and used to evaluate the performance of the best model.The central bigeye tuna fishing ground was determined by the nuclear density analysis tool of ArcGIS.The results show that(1)compared with the single model(KNN,LR,CART,SVM,ANN,RF and GBDT),the forecasting perform

关 键 词：大眼金枪鱼 延绳钓 渔情预报 集成学习 大西洋 

分 类 号：S934[农业科学—渔业资源]