基于CNN-BiLSTM模型的黄鳍金枪鱼渔获量与气候因子关系研究  

Study on relationship between Thunnus albacares catches and climatic factors based on CNN-BiLSTM model

作　　者：丁鹏 邹晓荣[1,2,3,4,5] 丁淑仪 白思琦 DING Peng;ZOU Xiaorong;DING Shuyi;BAI Siqi(College of Marine Living Resource Sciences and Management,Shanghai Ocean University,Shanghai 201306,China;Collaborative Innovation Center for National Distant-Water Fisheries,Shanghai 201306,China;Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources,Ministry of Education,Shanghai 201306,China;National Distant-Water Fisheries Engineering Research Center,Shanghai 201306,China;Scientific Observing and Experimental Station of Oceanic Fishery Resources,Ministry of Agriculture and Rural Affairs,Shanghai 201306,China;School of Education,Shangdong Women's University,Jinan 250300,China)

机构地区：[1]上海海洋大学海洋生物资源与管理学院,上海201306 [2]远洋渔业创新中心,上海201306 [3]大洋渔业资源可持续开发省部共建教育部重点实验室,上海201306 [4]国家远洋渔业工程技术研究中心,上海201306 [5]农业农村部大洋渔业资源环境科学观测试验站,上海201306 [6]山东女子学院教育学院,山东济南250300

出　　处：《南方水产科学》2024年第2期19-26,共8页South China Fisheries Science

基　　金：浙江省“领雁”重大攻关计划项目(2022C02025)。

摘　　要：为探究气候因子对黄鳍金枪鱼渔获量的影响,根据1960—2021年的南方涛动指数(SOI)、太平洋年代际涛动(PDO)、北大西洋涛动(NAO)、北太平洋指数(NPI)、全球海气温度异常指标(dT)以及厄尔尼诺相关指标(Niño1+2、Niño3、Niño4以及Niño3.4)等9种气候因子数据和全球黄鳍金枪鱼渔获量数据,采用相关性分析、BP神经网络、长短期记忆网络(LSTM)模型、双向长短期记忆网络(BiLSTM)模型和卷积神经网络结合双向长短期记忆网络(CNN-BiLSTM)模型对低频气候因子与黄鳍金枪鱼渔获量的关系进行了研究。结果表明,气候变化表征因子对黄鳍金枪鱼渔获量的重要性依次为dT>SOI>Niño1+2>PDO>NPI>NAO,其对应的最佳滞后年限分别为0、11、6、5、15、0年。CNNBiLSTM模型的预测效果最优,其后依次为BiLSTM模型、LSTM模型、BP神经网络模型。最优预测模型显示预测值与实际值的拟合优度为0.887,平均绝对误差为0.125,均方根误差为0.154,预测值与实际值变化趋势基本一致,模型拟合效果良好。To explore the impact of climatic factors on Thunnus albacares catches,we studied its relationship with low-frequency climatic factors by using correlation analysis,BP neural network,LSTM model,BiLSTM model and CNN-BiLSTM model based on the data of nine climate factors,including Southern Oscillation Index(SOI),Pacific Decadal Oscillation(PDO),North Atlantic Oscillation(NAO),North Pacific Index(NPI),global sea-air temperature anomaly index(dT),El Niño-related indexes(Niño1+2,Niño3,Niño4,Niño3.4)from 1960 to 2021,as well as global T.albacares catches data.The results show that the importance of climate change characterization factors on T.albacares catches followed a descending order of dT>SOI>Niño1+2>PDO>NPI>NAO,whose corresponding optimal lag periods were 0,11,6,5,15 and 0 years,respectively.CNN-BiLSTM model had the highest prediction accuracy,followed by BiLSTM,LSTM and BP.The goodness of fit between the predicted and actual values of CNN-BiLSTM model was 0.887,with a mean absolute error of 0.125 and a root mean square error of 0.154.The trend of predicted values and actual values was basically consistent,indicating a good model fitting effect.

关 键 词：黄鳍金枪鱼 渔获量 气候因子 CNN-BiLSTM模型 相关性分析 

分 类 号：S931.9[农业科学—渔业资源]