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-BiL-STM)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.CNN-BiLSTM 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.
Abstract
To explore the impact of climatic factors on Thunnus albacares catches,we studied its relationship with low-fre-quency climatic factors by using correlation analysis,BP neural network,LSTM model,BiLSTM model and CNN-BiLSTM mo-del 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.
基金项目
浙江省"领雁"重大攻关计划项目(2022C02025)
NETL
NSTL
维普
万方数据