| 基于主成分和BP神经网络的智利竹筴鱼渔场预报模型研究 |
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| 引用本文： | 汪金涛,高峰,雷林,邹晓荣,官文江,陈新军.基于主成分和BP神经网络的智利竹筴鱼渔场预报模型研究[J].海洋学报,2014,36(8):65-71. |
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| 作者姓名： | 汪金涛 高峰 雷林 邹晓荣 官文江 陈新军 |
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| 作者单位： | 1.上海海洋大学 海洋科学学院, 上海 201306;远洋渔业协同创新中心, 上海 201306 |
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| 基金项目： | 国家863计划（2012AA092301）；国家发改委产业化专项（2159999）；上海市科技创新行动计划（12231203900）和国家科技支撑计划（2013BAD13B01）。 |
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| 摘 要： | 东南太平洋智利竹筴鱼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筴鱼的渔捞日志数据,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筴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%。
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| 关 键 词： | 东南太平洋 智利竹筴鱼 BP神经网络 主成分分析 渔场预报 |
| 收稿时间： | 5/7/2013 12:00:00 AM |
| 修稿时间： | 2014/1/11 0:00:00 |
Application of BP neural network based on principal component analysis in fishing grounds of chilean jack mackerel (Trachurus murphyi) in the southeast Pacific Ocean |
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| Wang Jintao,Gao Feng,Lei Lin,Zou Xiaorong,Guan Wenjiang and Chen Xinjun.Application of BP neural network based on principal component analysis in fishing grounds of chilean jack mackerel (Trachurus murphyi) in the southeast Pacific Ocean[J].Acta Oceanologica Sinica (in Chinese),2014,36(8):65-71. |
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| Authors: | Wang Jintao Gao Feng Lei Lin Zou Xiaorong Guan Wenjiang and Chen Xinjun |
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| Affiliation: | College of Marine Sciences of Shanghai Ocean University, Shanghai 201306, China;Collaborative Innovation Center for Distant-water Fisheries, Shanghai 201306, China;College of Marine Sciences of Shanghai Ocean University, Shanghai 201306, China;The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Shanghai Ocean University, Ministry of Education, Shanghai 201306, China;National Distant-water Fisheries Engineering Research Center, Shanghai Ocean University, Shanghai 201306, China;Collaborative Innovation Center for Distant-water Fisheries, Shanghai 201306, China;College of Marine Sciences of Shanghai Ocean University, Shanghai 201306, China;The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Shanghai Ocean University, Ministry of Education, Shanghai 201306, China;National Distant-water Fisheries Engineering Research Center, Shanghai Ocean University, Shanghai 201306, China;Collaborative Innovation Center for Distant-water Fisheries, Shanghai 201306, China;College of Marine Sciences of Shanghai Ocean University, Shanghai 201306, China;The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Shanghai Ocean University, Ministry of Education, Shanghai 201306, China;National Distant-water Fisheries Engineering Research Center, Shanghai Ocean University, Shanghai 201306, China;Collaborative Innovation Center for Distant-water Fisheries, Shanghai 201306, China;College of Marine Sciences of Shanghai Ocean University, Shanghai 201306, China;The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Shanghai Ocean University, Ministry of Education, Shanghai 201306, China;National Distant-water Fisheries Engineering Research Center, Shanghai Ocean University, Shanghai 201306, China;Collaborative Innovation Center for Distant-water Fisheries, Shanghai 201306, China;College of Marine Sciences of Shanghai Ocean University, Shanghai 201306, China;The Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Shanghai Ocean University, Ministry of Education, Shanghai 201306, China;National Distant-water Fisheries Engineering Research Center, Shanghai Ocean University, Shanghai 201306, China;Collaborative Innovation Center for Distant-water Fisheries, Shanghai 201306, China |
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| Abstract: | 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 guidance for fishing operation. In this paper, we built the forecasting models by using the methods of principal component analysis (PCA) and BP neural networks according to the catch data from the logbooks and fishing yield statistics 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 factors. We also determined the two suitable model structures by using the original-samples and PCA-processed-samples combined with the accuracy of models, respectively. It is found that the model used by PCA-processed-samples 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. |
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| Keywords: | southeast Pacific Trachurus murphyi BP neural network principal component analysis fishing ground forecasting |
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