基于神经网络的西南大西洋阿根廷滑柔鱼渔场预报模型比较
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- 英文论文题名：Forecasting fishing ground of Illex argentinus based on artificial neural networks in the Southwest Atlantic
- 论文作者：李娜 ; 陆化杰 ; 陈新军
- 英文论文作者：LI Na ; LU Huajie ; CHEN Xinjun ; College of Marine Sciences of Shanghai Ocean University
- 年：2016
- 作者机构：上海海洋大学海洋科学学院;大洋渔业资源可持续开发省部共建教育部重点实验室;国家远洋渔业工程技术研究中心;远洋渔业协同创新中心;
- 论文关键词：西南大西洋 ; 阿根廷滑柔鱼 ; BP神经网络 ; 渔场预报
- 英文论文关键词：Southwest Atlantic ; Illex argentinus ; BP artificial neural network ; forecasting fishing ground
- 会议召开时间：2016-11-02
- 会议录名称：2016年中国水产学会学术年会论文摘要集
- 语种：中文
- 分类号：S934
- 学会代码：OGSB
- 会议名称：2016年中国水产学会学术年会
- 会议地点：中国四川成都
- 主办单位：中国水产学会、四川省水产学会
- 学会名称：中国水产学会
- 页数：1
- 文件大小：505k
- 原文格式：D
- 会议级别：全国
摘要
阿根廷滑柔鱼是重要的经济头足类,其渔场分布与海洋环境因子关系密切,建立高精度的预报模型对渔业生产极为重要。本文根据2003‐2011年渔汛期间我国鱿钓船在西南大西洋海域的生产统计数据,结合海洋遥感获得的海表温度(SST)和海面高度(SSH)等数据,以单位捕捞努力量渔获量(CPUE)和作业次数作为中心渔场指标,以月份、经度、纬度、SST和SSH为输入因子,利用BP神经网络方法构建西南大西洋阿根廷滑柔鱼中心渔场预报模型。对14种不同结构的BP神经网络模型比较认为,输入因子为月份、经度、纬度、SST和SSH,输出因子为初值化后的CPUE,网络结构为5-4-1时的BP神经网络模型为最佳。
Illes argentinus is an important economic cephalopod in the world. Its distribution of fishing ground is closely related to the marine environmental factors, 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 BP neural networks with the catch data, and remote sensing data including sea surface temperature(SST) and sea surface height(SSH) from November to August of next year during the years of 2003 to 2011. The BP neural networks model was applied to predict the distribution of fishing grounds of Illex argentinus based on the standardization of CPUE and fishing effort. In order to compare the BP models, fishing month,fishing position(latitude and longitude), SST and SSH were randomly used as inputting factors.The total of 14 models with different hidden layers had been tested and compared. It was found that the model with 5-4-1 networks structure could be used for better predicting fishing ground of Illex argentinus, in which the standardization of CPUE was considered as the output factor, and month, latitude, longitude, SST and SSH were used as the inputting factors.
Illes argentinus is an important economic cephalopod in the world. Its distribution of fishing ground is closely related to the marine environmental factors, 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 BP neural networks with the catch data, and remote sensing data including sea surface temperature(SST) and sea surface height(SSH) from November to August of next year during the years of 2003 to 2011. The BP neural networks model was applied to predict the distribution of fishing grounds of Illex argentinus based on the standardization of CPUE and fishing effort. In order to compare the BP models, fishing month,fishing position(latitude and longitude), SST and SSH were randomly used as inputting factors.The total of 14 models with different hidden layers had been tested and compared. It was found that the model with 5-4-1 networks structure could be used for better predicting fishing ground of Illex argentinus, in which the standardization of CPUE was considered as the output factor, and month, latitude, longitude, SST and SSH were used as the inputting factors.
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