基于集成学习的大西洋热带水域大眼金枪鱼渔情预报

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...

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

In order to improve the accuracy of bigeye tuna (Thunnus obesus) fishing ground forecast 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. 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) were built by using 75 % of data and verified by using 25 % of data. The results show that: (1) Compared with single model (KNN, LR, CART, SVM, ANN, RF and GBDT), the forecasting performance of bigeye tuna fishing ground of Stacking ensemble model was better and relatively stable. And the accuracy (AUC, the area under the ROC curve) of the Stacking ensemble model, KNN, LR, CART, SVM, ANN, RF and GBDT were 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 % (0.782), and 78.13 % (0.747), respectively; (2) The distribution of central fishing ground predicted by Stacking ensemble model was basically consistent with the actual distribution of central fishing ground, all of them were mainly distributed in the area of 3 °N~15 °N, 30 °W~45 °W; (3) The marine environmental factors that affect the distribution of bigeye tuna fishing grounds in the Atlantic Ocean mainly included dissolved oxygen of 300 m layer, salinity of 500 m layer, sea surface wind speed and upper boundary depth of thermocline, and the relative importance were as follows: 13.24 %, 9.12 %, 9.12 % and 8.81 %, respectively. The results suggest that the accuracy of the Stacking ensemble model for bigeye tuna fishing ground forecast in the Atlantic Ocean is high.