基于BP神经网络的西北太平洋柔鱼资源丰度时空变化研究

王雅萌; 汪金涛; 陈新军; 雷林

doi:10.12284/hyxb2021106

基于BP神经网络的西北太平洋柔鱼资源丰度时空变化研究

doi: 10.12284/hyxb2021106

王雅萌^1,,
汪金涛^{1, 2, 3, 4, 5, ,},
陈新军^{1, 2, 3, 4, 5},
雷林^{1, 2, 3, 4, 5}

1.
上海海洋大学海洋科学学院，上海 201306
2.
农业部大洋渔业开发重点实验室，上海 201306
3.
上海海洋大学国家远洋渔业工程技术研究中心，上海 201306
4.
上海海洋大学大洋渔业资源可持续开发教育部重点实验室，上海 201306
5.
农业部大洋渔业资源环境科学观测实验站，上海 201306

基金项目: 国家重点研发计划（2019YFD0901404）；国家自然科学基金（NSFC41876141）；上海市科技创新行动计划（19DZ1207502）；自然资源卫星遥感技术体系建设与应用示范项目（202001004）

详细信息

作者简介:
王雅萌(1996-)，女，新疆维吾尔自治区乌鲁木齐市人，研究方向为渔业资源。E-mail：1055466034@qq.com

通讯作者:
汪金涛，讲师，主要从事人工智能渔业学研究。E-mail: jtwang@shou.edu.cn

中图分类号: S931.4
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- 文章访问数: 533
- HTML全文浏览量: 114
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- 被引次数: 0
出版历程
- 收稿日期: 2019-11-18
- 修回日期: 2020-04-23
- 网络出版日期: 2021-05-08
- 刊出日期: 2021-06-30

Spatio-temporal dynamic of abundance index of Neon flying squid in relation to environmental variables in the Northwest Pacific Ocean using BP neural network

Wang Yameng^1
,,
Wang Jintao^{1, 2, 3, 4, 5
, ,},
Chen Xinjun^{1, 2, 3, 4, 5},
Lei Lin^{1, 2, 3, 4, 5}

1.
College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
2.
Key Laboratory of Oceanic Fisheries Exploration, Ministry of Agriculture, Shanghai 201306, China
3.
National Engineering Research Center for Oceanic Fisheries, Shanghai Ocean University, Shanghai 201306, China
4.
Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai Ocean University, Shanghai 201306, China
5.
Scientific Observing and Experimental Station of Oceanic Fishery Resources, Ministry of Agriculture, Shanghai 201306, China

摘要

摘要: 西北太平洋柔鱼(Ommastrephes bartramii)是我国鱿钓船队重要捕捞对象，其资源分布的时空变化对资源的可持续开发和利用有重要影响。本文以2000−2015年7−10月西北太平洋柔鱼渔业数据的单位捕捞努力量渔获量作为应变量，以年份、月份、经度、纬度、海表面温度、海表盐度、海面高度、叶绿素a浓度作为自变量，构建BP神经网络模型，推测该段时间西北太平洋柔鱼丰度时空变化规律，并探究环境因子对柔鱼资源丰度的影响。通过比较，确定输入层为年份、月份、经度、纬度、海表面温度和海表盐度，隐含层神经元数量为8的模型均方误差最小，模型最优。结果表明，单位捕捞努力量渔获量年间波动明显，每年的7月、10月柔鱼资源丰度较低，且空间分布分散在整个作业渔场，8月、9月资源丰度较高，并集中在41.5°～43.5°N, 155°～160°E局部区域。研究认为，海表面温度和海表盐度对柔鱼资源丰度时空变动有较大影响，在今后柔鱼资源评估与管理中予以考虑。
- 西北太平洋 /
- BP神经网络 /
- 时空变化 /
- 标准化
Abstract: Ommastrephes bartramii is an economically important species for Chinese squid jigging fleet. Understanding the spatio-temporal distribution on fishing ground is crucial to the sustainable utilization of fish resources. The study constructed BP (back propagation) neural network models with different scenarios to speculate the dynamics of O. bartramii abundance based on fishery data in the months of July to October during 2000 to 2015. The model with year, month, longitude, latitude, sea surface temperature (SST), and sea surface salinity (SSS) as independent variables, 8 neurons in hidden layers, had the smallest mean square error, and thus selected as optimal model. The results showed that the significant fluctuation in CPUE between years, the local abundance was low and scattered in July and October, whereas was high and concentrated at 41.5°−43.5°N, 155°−160°E in August and September. Environmental factors, including SST and SSS affect the spatio-temporal distribution of local abundance, and should be considered in stock assessment and management.
- Northwest Pacific /
- back propagation neural network /
- spatial-temporal change /
- standardization

HTML全文

图 1 西北太平洋柔鱼冬生群体空间和洄游分布

Fig. 1 Spatial distribution and migratory distribution of winter cohort of Ommastrephes bartramii in the Northwest Pacific Ocean

下载: 全尺寸图片幻灯片

图 2 西北太平洋柔鱼空间BP神经网络最优模型训练样本集（PCPUE）拟合状态

Fig. 2 Training dataset fitting of optimal BP neural network (PCPUE) for Ommastrephes bartramii in the Northwest Pacific Ocean

下载: 全尺寸图片幻灯片

图 3 西北太平洋柔鱼空间BP神经网络最优模型测试样本集（PCPUE）拟合状态

Fig. 3 Testing dataset fitting of optimal BP neural network (PCPUE) for Ommastrephes bartramii in the Northwest Pacific Ocean

下载: 全尺寸图片幻灯片

图 4 西北太平洋柔鱼最优空间BP神经网络模型

Fig. 4 Optimal spatial BP neural network model for Ommastrephes bartramii in the Northwest Pacific Ocean

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图 5 2000−2008年西北太平洋柔鱼预测CPUE分布

Fig. 5 Simulated CPUE distribution of Ommastrephes bartramii from 2000 to 2008 in the Northwest Pacific Ocean

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图 6 2009−2015年西北太平洋柔鱼预测CPUE分布

Fig. 6 Simulated CPUE distribution of Ommastrephes bartramii from 2009 to 2015 in the Northwest Pacific Ocean

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图 7 2000−2015年7−10月份柔鱼资源丰度时间趋势

Fig. 7 Temporal trend of the abundance of CPUE of Ommastrephes bartramii from July to October during 2000 to 2015

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表 1 西北太平洋柔鱼空间分布BP神经网络模型

Tab. 1 Spatial BP neural network models for Ommastrephes bartramii in the Northwest Pacific Ocean

方案	输入层因子	BP神经网络模型结构
1	经度、纬度、年份、月份、SST	5-4-1、5-5-1、5-6-1、5-7-1、5-8-1、 5-9-1、5-10-1、5-11-1、5-12-1
2	经度、纬度、年份、月份、SST、SSS	6-4-1、6-5-1、6-6-1、6-7-1、6-8-1、 6-9-1、6-10-1、6-11-1、6-12-1
3	经度、纬度、年份、月份、SST、SSS、Chl a浓度	7-4-1、7-5-1、7-6-1、7-7-1、7-8-1、 7-9-1、7-10-1、7-11-1、7-12-1
4	经度、纬度、年份、月份、SST、SSS、Chl a浓度、SSH	8-4-1、8-5-1、8-6-1、8-7-1、8-8-1、8-9-1、8-10-1、8-11-1、8-12-1、8-13-1
注：BP神经网络模型结构中数字分别表示输入层、隐含层和输出层神经元个数。

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表 2 不同方案不同隐含层下训练集CPUE的均方误差

Tab. 2 Mean square error of training dataset CPUE for different scenarios

隐含层数	方案
隐含层数	1	2	3	4
4	0.042 1	0.041 7	0.043 6	0.045 3
5	0.041 6	0.040 9	0.043 1	0.044 5
6	0.041 0	0.039 5	0.042 5	0.043 8
7	0.040 2	0.038 8	0.042 1	0.043 5
8	0.039 4	0.038 2	0.041 9	0.042 9
9	0.038 9	0.038 4	0.041 7	0.042 6
10	0.039 2	0.038 6	0.041 9	0.043 0
11	0.040 4	0.039 2	0.042 4	0.043 2
12	0.041 3	0.041 5	0.042 9	0.043 8
13	0.041 7	0.042 0	0.043 2	0.044 5

下载: 导出CSV

表 3 不同方案不同隐含层下测试集CPUE的均方误差

Tab. 3 Mean square error of testing dataset CPUE for different scenarios

隐含层数	方案
隐含层数	1	2	3	4
4	0.041 8	0.041 3	0.043 3	0.044 7
5	0.041 3	0.040 7	0.042 8	0.044 0
6	0.040 6	0.039 2	0.042 2	0.043 4
7	0.039 9	0.038 6	0.041 9	0.043 1
8	0.039 2	0.038 0	0.041 6	0.042 6
9	0.038 8	0.038 3	0.041 4	0.042 3
10	0.039 0	0.038 4	0.041 6	0.042 7
11	0.040 0	0.038 8	0.042 1	0.042 9
12	0.041 1	0.040 7	0.042 7	0.043 3
13	0.041 5	0.041 6	0.043 0	0.044 0

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