Application of BP neural network based on principal component analysis in fishing grounds of chilean jack mackerel (<em>Trachurus murphyi</em>) in the southeast Pacific Ocean

Wang Jintao; Gao Feng; Lei Lin; Zou Xiaorong; Guan Wenjiang; Chen Xinjun

doi:10.3969/j.issn.0253-4193.2014.08.007

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Wang Jintao, Gao Feng, Lei Lin, Zou Xiaorong, Guan Wenjiang, 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]. Haiyang Xuebao, 2014, 36(8): 65-71. doi: 10.3969/j.issn.0253-4193.2014.08.007

Citation:

Wang Jintao, Gao Feng, Lei Lin, Zou Xiaorong, Guan Wenjiang, 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]. Haiyang Xuebao, 2014, 36(8): 65-71. doi: 10.3969/j.issn.0253-4193.2014.08.007

Citation:

Wang Jintao, Gao Feng, Lei Lin, Zou Xiaorong, Guan Wenjiang, 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]. Haiyang Xuebao, 2014, 36(8): 65-71. doi: 10.3969/j.issn.0253-4193.2014.08.007

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Application of BP neural network based on principal component analysis in fishing grounds of chilean jack mackerel (Trachurus murphyi) in the southeast Pacific Ocean

doi: 10.3969/j.issn.0253-4193.2014.08.007

1.
College of Marine Sciences of Shanghai Ocean University, Shanghai 201306, China;Collaborative Innovation Center for Distant-water Fisheries, Shanghai 201306, China
2.
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

Received Date: 2013-05-07
Rev Recd Date: 2014-01-11

Abstract

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.
- southeast Pacific,
- Trachurus murphyi,
- BP neural network,
- principal component analysis,
- fishing ground forecasting

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References(31)

References

邹莉瑾, 张敏, 邹晓荣, 等. 东南太平洋公海智利竹筴鱼年龄与生长研究[J]. 上海海洋大学学报, 2010, 19(1): 61-67.

方宇, 邹晓荣, 张敏, 等. 东南太平洋智利竹筴鱼栖息地指数的比较研究[J]. 海洋渔业, 2010, 32(2): 178-185.

FAO. The state of world fisheries and aquaculture [R]. Rome: FAO, 1996-2007.

牛明香, 李显森, 徐玉成. 基于广义可加模型和案例推理的东南太平洋智利竹筴鱼中心渔场预报[J]. 海洋环境科学, 2012, 31(1): 30-33.

崔雪森, 伍玉梅, 张晶, 等. 基于分类回归树算法的东南太平洋智利竹筴鱼渔场预报[J]. 中国海洋大学学报 (自然科学版), 2012, 42(7/8): 53-59.

张衡, 崔雪森, 樊伟. 基于遥感数据的智利竹筴鱼渔场预报系统[J]. 农业工程学报, 2012, 28(15): 140-144.

陈新军, 赵小虎. 西南大西洋阿根廷滑柔鱼产量分布与表温关系的初步研究[J]. 大连水产学院学报, 2005, 20(3): 222-228.

方舟, 陈新军, 李建华, 等. 阿根廷专属经济区内鱿钓渔场分布及其与表温关系[J]. 上海海洋大学学报, 2013, 22(1): 134-140.

高峰, 陈新军, 范江涛, 等. 西南大西洋阿根廷滑柔鱼中心渔场预报的实现及验证[J]. 上海海洋大学学报, 2011, 20(5): 754-758.

陈新军, 陆化杰, 刘必林, 等. 利用栖息地指数预测西南大西洋阿根廷滑柔鱼渔场[J]. 上海海洋大学学报, 2012, 21(3): 431-438.

王为祥, 朱德山. 黄海鲐鱼渔业生物学研究:Ⅱ. 黄、渤海鲐鱼行动分布与环境关系的研究[J]. 海洋水产研究, 1984(6): 59-76.

韦晟, 周彬彬. 黄渤海蓝点马鲛短期渔情预报的研究[J]. 海洋学报, 1988, 10(2): 216-221.

陈新军, 冯波, 许柳雄. 印度洋大眼金枪鱼栖息地指数研究及其比较[J]. 中国水产科学, 2008, 15(2): 269-278.

邵全琴, 马巍巍, 陈卓奇, 等. 西北太平洋黑潮路径变化与柔鱼CPUE的关系研究[J]. 海洋与湖沼, 2005, 36(2): 111-122.

刘传桢, 严隽箕, 崔维喜. 渤海秋汛对虾数量预报方法的研究[J]. 水产学报, 1981, 5(1): 65-73.

陈新军, 刘必林, 田思泉, 等. 利用基于表温因子的栖息地模型预测西北太平洋柔鱼(Ommastrephes bartramii)渔场[J]. 海洋与湖沼, 2009, 40(6): 707-713.

冯波, 田思泉, 陈新军. 基于分位数回归的西南太平洋阿根廷滑柔鱼栖息地模型研究[J]. 海洋湖沼通报, 2010(1): 15-22.

樊伟, 崔雪森, 沈新强. 渔场渔情分析预报的研究及其进展[J]. 水产学报, 2005, 29(5): 706-710.

易倩, 陈新军. 基于信息增益法选取柔鱼中心渔场的关键水温因子[J]. 上海海洋大学学报, 2012, 21(3): 425-430.

梁娜. 基于神经网络与主成分分析的组合预测研究[D]. 武汉: 武汉理工大学, 2007.

Johnson R A, Wichern D W. Applied Multivariate Statistical Analysis[M]. Upper Saddle River, NJ: Prentice Hall, 2002.

何晓群. 现代统计分析方法与应用[M]. 北京: 中国人民大学出版社, 2007.

于秀林, 任雪松. 多元统计分析[M]. 北京: 中国统计出版社, 1999.

Benediktsson J A, Swain P H, Ersoy O K. Neural network approaches versus statistical methods in classification of multisource remote sensing data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1990, 28(4): 540-552.

Hush D R, Horne B G. Progress in supervised neural networks[J]. IEEE Signal Processing Magazine, 1993, 10(1): 8-39.

刘维群, 李元臣. BP网络中隐含层节点优化的研究[J]. 交通与计算机, 2005, 23(2): 83-86.

化成君, 张衡, 樊伟. 东南太平洋智利竹筴鱼资源和渔场的时空变化[J]. 生态学报, 2011, 31(19): 5676-5681.

张衡, 张胜茂. 东南太平洋智利竹筴鱼渔场及单位捕捞努力量的时空分布[J]. 生态学杂志, 2011, 30(6): 1142-1146.

牛明香, 李显森, 徐玉成. 基于广义可加模型的时空和环境因子对东南太平洋智利竹筴鱼渔场的影响[J]. 应用生态学报, 2010, 21(4): 1049-1055.

Olden J D, Jackson D A. Illuminating the "black box": a randomization approach for understanding variable contributions in artificial neural networks[J]. Ecological Modelling, 2002, 154(1): 135-150.

Özesmi S L, Özesmi U. An artificial neural network approach to spatial habitat modelling with interspecific interaction[J]. Ecological Modelling, 1999, 116(1): 15-31.

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