CAJ | 학술논문

东南太平洋智利竹？鱼 Trachurus murphyi 是我国大型拖网渔船队的重要捕捞对象。准确预报中心渔场是提高渔业生产能力的重要工作。本文根据2003-2009年我国船队在东南太平洋海域捕捞智利竹？鱼的渔捞日志数据，结合海洋遥感获得的海表温度（SST）和海面高度（SSH）等海洋环境因子，利用主成分和 BP 神经网络方法对智利竹？鱼中心渔场预报模型进行了研究。研究利用主成分分析法（PCA）得到累计贡献率在90％以上样本的主成分，综合考虑模型测试的精度与速度，基于原始样本和经 PCA 处理后的主成分分别建立了 BP 模型，其最优 BP 模型结构分别为5∶10∶1和3∶7∶1。研究结果表明，经 PCA 处理后的主成分所建立的 BP 神经网络模型在训练结果和测试结果上均要优于用原始样本建立的 BP 神经网络模型，两者的预报准确率分别为67％和60％。
동남태평양지리죽？어 Trachurus murphyi 시아국대형타망어선대적중요포로대상。준학예보중심어장시제고어업생산능력적중요공작。본문근거2003-2009년아국선대재동남태평양해역포로지리죽？어적어로일지수거，결합해양요감획득적해표온도（SST）화해면고도（SSH）등해양배경인자，이용주성분화 BP 신경망락방법대지리죽？어중심어장예보모형진행료연구。연구이용주성분분석법（PCA）득도루계공헌솔재90％이상양본적주성분，종합고필모형측시적정도여속도，기우원시양본화경 PCA 처리후적주성분분별건립료 BP 모형，기최우 BP 모형결구분별위5∶10∶1화3∶7∶1。연구결과표명，경 PCA 처리후적주성분소건립적 BP 신경망락모형재훈련결과화측시결과상균요우우용원시양본건립적 BP 신경망락모형，량자적예보준학솔분별위67％화60％。
Chilean jack mackerel (Trachurus murphyi )is an important target species for Chinese factory trawler fleet in the southeast Pacific Ocean,and the accurate forecasting of fishing ground can provide better scientific guid-ance for fishing operation.In this paper,we built the forecasting models by using the methods of principal compo-nent analysis (PCA)and BP neural networks according to the catch data from the logbooks and fishing yield statis-tics from Chinese factory trawler fleets,the sea surface temperature (SST)and sea surface height (SSH)obtained by satellite remote sensing from 2003 to 2009.Based on the PCA,we got the principal components of different fac-tors.We also determined the two suitable model structures by using the original-samples and PCA-processed-sam-ples combined with the accuracy of models,respectively.It is found that the model used by PCA-processed-sam-ples is better than that model used by original-sampled based on the results of training and test,and their accuracy rates were 67% and 60% respectively.