基于BP神经网络的长鳍金枪鱼渔获量与气候因子关系研究

丁鹏; 邹晓荣; 许回; 丁淑仪; 白思琦; 张子辉

doi:10.12284/hyxb2024100

基于BP神经网络的长鳍金枪鱼渔获量与气候因子关系研究

doi: 10.12284/hyxb2024100

丁鹏^1,,
邹晓荣^{1, 2, 3, ,},
许回⁴,
丁淑仪⁵,
白思琦¹,
张子辉⁶

1.
上海海洋大学海洋生物资源与管理学院，上海 201306
2.
远洋渔业创新中心，上海 201306
3.
大洋渔业资源可持续开发省部共建教育部重点实验室，上海 201306
4.
北海道大学水产科学研究院，日本北海道 0418611
5.
山东女子学院教育学院，山东济南 250300
6.
山东鲁抗医药股份有限公司，山东济宁 272100

基金项目: 渔业生产数据收集（D-8002-12-0127-2）。

详细信息

作者简介:
丁鹏（1994—），男，山东省淄博市人，研究方向为渔业资源学。E-mail：282207687@qq.com

通讯作者:
邹晓荣（1971—），男，硕士，副教授，从事捕捞学研究。E-mail：xrzou@shou.edu.cn

中图分类号: S931.9
计量
- 文章访问数: 73
- HTML全文浏览量: 28
- PDF下载量: 9
- 被引次数: 0
出版历程
- 收稿日期: 2023-10-05
- 修回日期: 2024-05-09
- 网络出版日期: 2024-08-15
- 刊出日期: 2024-09-01

Study on the relationship between catch of Thunnus alalunga and climatic factors based on BP neural network

Ding Peng^1
,,
Zou Xiaorong^{1, 2, 3
, ,},
Xu Hui⁴,
Ding Shuyi⁵,
Bai Siqi¹,
Zhang Zi Hui⁶

1.
College of Marine Living Resource Sciences and Management, Shanghai Ocean University, Shanghai 201306, China
2.
Collaborative Innovation Center for National Distant-water Fisheries, Shanghai 201306, China
3.
Key Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai 201306, China
4.
Hokkaido University, Graduate School of Fisheries Science, Japan Hokkaido
5.
School of Education, Shandong Women’s University, Jinan 250300, China
6.
Shandong Lukang Pharmaceutical Co, JiNing 277100, China

摘要

摘要: 为探讨气候变化对长鳍金枪鱼渔获量的影响，利用中西太平洋渔业委员会统计的1960−2021年太平洋长鳍金枪鱼年度渔获量和对应的厄尔尼诺指标（Niño1+2、Niño3、Niño4以及Niño3.4）、南方涛动指数（SOI）、北大西洋涛动（NAO）、太平洋年代际涛动（PDO）、北太平洋指数（NPI）以及全球海气温度异常指标（dT）等月度数据，采用BP神经网络和变量敏感性分析法探讨了低频气候因子与长鳍金枪鱼渔获量的关系；构建了结构为6-8-1的最优BP神经网络模型，对长鳍金枪鱼渔获量进行了预测。结果表明，Niño1+2、SOI、NAO、PDO、NPI、dT为影响长鳍金枪鱼渔获量相对独立的气候因子，其对应的最佳滞后阶数依次为8年、2年、9年、0年、9年、3年。Niño1+2、SOI、NAO为影响长鳍金枪鱼渔获量的关键气候因子。长鳍金枪鱼渔获量预测值和实际值差值与实际值的比值自1971年后基本维持在15%以内，预测值与实际值变化趋势基本一致，模型拟合效果良好。
- 气候变化 /
- 长鳍金枪鱼 /
- 相关性分析 /
- BP神经网络
Abstract: In order to investigate the impact of climate change on the catch of bigeye tuna, we utilized the annual Pacific bigeye tuna catch data from 1960 to 2021, which was statistically compiled by the Western and Central Pacific Fisheries Commission. We also employed corresponding monthly climate indices, including Niño1+2, Niño3, Niño4, Niño3.4, Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), North Pacific Index (NPI), and global sea-air temperature anomaly (dT). By using a BP neural network and variable sensitivity analysis, we examined the relationship between these low-frequency climate factors and bigeye tuna catch. Our findings revealed that Niño1+2, SOI, NAO, PDO, NPI, and dT are relatively independent climate factors that have an impact on bigeye tuna catch. The optimal lag orders for these climate factors were determined to be 8 years for Niño1+2, 2 years for SOI, 9 years for NAO, 0 years for PDO, 9 years for NPI, and 3 years for dT. Among these factors, Niño1+2, SOI, and NAO were identified as the key climate factors influencing bigeye tuna catch. We constructed an optimal BP neural network model with a structure of 6-8-1, and the ratio of the difference between the predicted and actual bigeye tuna catch to the actual catch has been maintained within 15% since 1971. Additionally, the trend of the predicted and actual catch was found to be basically consistent, indicating a satisfactory level of model fit.
- climate change /
- Thunnus alalunga /
- correlation analysis /
- BP neural network model

HTML全文

图 1 Spearman秩相关系数表

Fig. 1 Spearman Rank Correlation Coefficient Table

下载: 全尺寸图片幻灯片

图 2 气候表征因子与长鳍金枪鱼互相关关系

Fig. 2 Cross-correlation between climate characterization factors and Thunnus alalunga catch

下载: 全尺寸图片幻灯片

图 3 BP神经网络模型的最优效率系数

Fig. 3 Efficiency coefficient of BP neural network model

下载: 全尺寸图片幻灯片

图 4 长鳍金枪鱼渔获量预测值与实际值对比

Fig. 4 Comparison between the observed catch and the fitted value of Thunnus alalunga

下载: 全尺寸图片幻灯片

图 5 长鳍金枪鱼渔获量实际值和预测值差值与实际值比值

Fig. 5 The ratio of the difference between the predicted and actual bigeye tuna catch to the actual catch

下载: 全尺寸图片幻灯片

图 6 气候变化表征因子敏感性分析

Fig. 6 Sensitivity analysis of climate change characterization factors

下载: 全尺寸图片幻灯片

参考文献(38)

[1]	官文江, 于永恒, 贺伟伟, 等. 长鳍金枪鱼全生命史动态能量收支预测模型[J/OL]. 水产学报, 2023: 1−15. http://kns.cnki.net/kcms/detail/31.1283.S.20230329.1408.002.html, 2023−09−23. Guan Wenjiang, Yu Yongheng, He Weiwei, et al. A full lifecycle dynamic energy budget model for albacore tuna[J/OL]. Journal of Fisheries of China, 2023: 1−15. http://kns.cnki.net/kcms/detail/31.1283.S.20230329.1408.002.html, 2023−09−23.
[2]	Lee L K, Hsu C C, Chang F C. Albacore (Thunnus alalunga) CPUE trend from Indian Core Albacore Areas based on Taiwanese longline catch and effort statistics dating from 1980 to 2013[J]. IOTC-2014-WPTmT05-19, 2014.
[3]	许回. 不同空间分辨率对LSTM渔场预报精度的影响及在最佳空间分辨率下其预报结果与QRM结果的比较——以库克群岛海域长鳍金枪鱼为例[D]. 上海: 上海海洋大学, 2022. Xu Hui. The impacts of different spatial resolutions on the fishing ground prediction accuracy of LSTM and the comparison between its prediction accuracy and that of the quantile regression model under the optimal spatial resolution—a case study on the Albacore tuna in the waters near Cook Islands[D]. Shanghai: Shanghai Ocean University, 2022.
[4]	Kimura S, Nakai M, Sugimoto T. Migration of albacore, Thunnus alalunga, in the North Pacific Ocean in relation to large oceanic phenomena[J]. Fisheries Oceanography, 1997, 6(2): 51−57. doi: 10.1046/j.1365-2419.1997.00029.x
[5]	袁红春, 陈冠奇, 张天蛟, 等. 基于全卷积网络的南太平洋长鳍金枪鱼渔场预报模型[J]. 江苏农业学报, 2020, 36(2): 423−429. doi: 10.3969/j.issn.1000-4440.2020.02.024 Yuan Hongchun, Chen Guanqi, Zhang Tianjiao, et al. Fishing ground forecast model of albacore tuna based on fully convolutional networks in the South Pacific[J]. Jiangsu Journal of Agricultural Sciences, 2020, 36(2): 423−429. doi: 10.3969/j.issn.1000-4440.2020.02.024
[6]	许回, 宋利明, 沈介然, 等. 基于GAM的库克群岛海域长鳍金枪鱼CPUE时空分布与海洋环境的关系[J]. 海洋通报, 2023, 42(4): 444−455. Xu Hui, Song Liming, Shen Jieran, et al. The relationship between the spatial-temporal distribution of albacore tuna CPUE and the marine environment variables in waters near the Cook Islands based on GAM[J]. Marine Science Bulletin, 2023, 42(4): 444−455.
[7]	Lu H J, Lee K T, Liao C H. On the relationship between El Niño/Southern Oscillation and South Pacific albacore[J]. Fisheries Research, 1998, 39(1): 1−7. doi: 10.1016/S0165-7836(98)00174-X
[8]	杨寒雨, 赵晓永, 王磊. 数据归一化方法综述[J]. 计算机工程与应用, 2023, 59(3): 13−22. doi: 10.3778/j.issn.1002-8331.2207-0179 Yang Hanyu, Zhao Xiaoyong, Wang Lei. Review of data normalization methods[J]. Computer Engineering and Applications, 2023, 59(3): 13−22. doi: 10.3778/j.issn.1002-8331.2207-0179
[9]	Panda S K, Nag S, Jana P K. A smoothing based task scheduling algorithm for heterogeneous multi-cloud environment[C]//Proceedings of 2014 International Conference on Parallel, Distributed and Grid Computing. Solan, India: IEEE, 2014: 62−67.
[10]	肖启华, 黄硕琳. 气候变化对东南太平洋智利竹筴鱼渔获量的影响[J]. 中国水产科学, 2021, 28(8): 1020−1029. Xiao Qihua, Huang Shuolin. Impact of climate change on Chilean jack mackerel catch in the Southeast Pacific[J]. Journal of Fishery Sciences of China, 2021, 28(8): 1020−1029.
[11]	余鹏明, 管孝艳, 陈俊英, 等. 基于Spearman秩相关的再生水利用量影响因素研究[J]. 节水灌溉, 2019(10): 78−82,88. doi: 10.3969/j.issn.1007-4929.2019.10.017 Yu Pengming, Guan Xiaoyan, Chen Junying, et al. Study on factors affecting reclaimed water utilization based on spearman rank correlation[J]. Water Saving Irrigation, 2019(10): 78−82,88. doi: 10.3969/j.issn.1007-4929.2019.10.017
[12]	肖启华. 气候变化背景下东南太平洋智利竹筴鱼资源评估模型研究[D]. 上海: 上海海洋大学, 2020. Xiao Qihua. Study on the assessment model of Chilean jack mackerel resources in the Southeast Pacific Ocean under the background of climate change[D]. Shanghai: Shanghai Ocean University, 2020.
[13]	甘雨, 马小川, 阎军. 空间互相关方法在分析海底沙波迁移规律中的应用[J]. 海洋学报, 2019, 41(4): 42−52. Gan Yu, Ma Xiaochuan, Yan Jun. The application of spatial cross correlation in analyzing the migration of submarine sand waves[J]. Haiyang Xuebao, 2019, 41(4): 42−52.
[14]	Lu H J, Lee K T, Lin H L, et al. Spatio-temporal distribution of yellowfin tuna Thunnus albacares and bigeye tuna Thunnus obesus in the tropical Pacific Ocean in relation to large-scale temperature fluctuation during ENSO episodes[J]. Fisheries Science, 2001, 67(6): 1046−1052. doi: 10.1046/j.1444-2906.2001.00360.x
[15]	陈国强, 申正义, 孙利, 等. 基于BP神经网络优化遗传算法的智能座舱感性意象预测[J]. 汽车工程, 2023, 45(8): 1479−1488. Chen Guoqiang, Shen Zhengyi, Sun Li, et al. Intelligent cockpit perceptual image prediction based on BP neural network optimization genetic algorithm[J]. Automotive Engineering, 2023, 45(8): 1479−1488.
[16]	李帅, 杨赫然, 孙兴伟, 等. 基于改进神经网络算法的数控钻攻中心进给轴热误差预测[J]. 电子测量与仪器学报, 2023, 37(9): 234−242. Li Shuai, Yang Heran, Sun Xingwei, et al. Prediction of thermal error of CNC drilling center feed axis based on improved neural network algorithm[J]. Journal of Electronic Measurement and Instrumentation, 2023, 37(9): 234−242.
[17]	丁鹏, 邹晓荣, 白思琦, 等. 东南太平洋智利竹筴鱼渔场时空分析与资源丰度的预测[J]. 大连海洋大学学报, 2021, 36(4): 629−636. Ding Peng, Zou Xiaorong R, Bai Siqi, et al. Spatial and temporal analysis and stock abundance prediction of Chilean jack mackerel Trachurus murphyi in fishing ground in Southeast Pacific[J]. Journal of Dalian Ocean University, 2021, 36(4): 629−636.
[18]	丁鹏, 邹晓荣, 冯超, 等. 东南太平洋智利竹筴鱼的洄游路线[J]. 大连海洋大学学报, 2021, 36(6): 1027−1034. Ding Peng, Zou Xiaorong, Feng Chao, et al. Migratory route of Chilean jack mackerel Trachurus murphyi in Southeast Pacific[J]. Journal of Dalian Ocean University, 2021, 36(6): 1027−1034.
[19]	王蕾, 王鹏新, 田苗, 等. 效率系数和一致性指数及其在干旱预测精度评价中的应用[J]. 干旱地区农业研究, 2016, 34(1): 229−235, 251. doi: 10.7606/j.issn.1000-7601.2016.01.35 Wang Lei, Wang Pengxin, Tian Miao, et al. Application of the coefficient of efficiency and index of agreement on accuracy assessment of drought forecasting models[J]. Agricultural Research in the Arid Areas, 2016, 34(1): 229−235, 251. doi: 10.7606/j.issn.1000-7601.2016.01.35
[20]	陈皓锐, 黄介生, 伍靖伟, 等. 灌溉用水效率尺度效应研究评述[J]. 水科学进展, 2011, 22(6): 872−880. Chen Haorui, Huang Jiesheng, Wu Jingwei, et al. Review of scale effect on the irrigation water use efficiency[J]. Advances in Water Science, 2011, 22(6): 872−880.
[21]	程懿麒, 张俊波, 汪金涛, 等. 基于神经网络的印度洋长鳍金枪鱼(Thunnus alalunga)时空分布与海洋环境关系研究[J]. 海洋与湖沼, 2021, 52(4): 960−970. doi: 10.11693/hyhz20210100003 Cheng Yiqi, Zhang Junbo, Wang Jintao, et al. Study on the relationship between temporal-spatial distribution of Indian Ocean albacore (Thunnus alalunga) and marine environment based on neural network[J]. Oceanologia et Limnologia Sinica, 2021, 52(4): 960−970. doi: 10.11693/hyhz20210100003
[22]	蔡毅, 邢岩, 胡丹. 敏感性分析综述[J]. 北京师范大学学报(自然科学版), 2008, 44(1): 9−16. Cai Yi, Xing Yan, Hu Dan. On sensitivity analysis[J]. Journal of Beijing Normal University (Natural Science), 2008, 44(1): 9−16.
[23]	王靓, 花传祥, 朱清澄, 等. 北太平洋小型中上层鱼类资源对气候–海洋变化的响应研究进展[J]. 中国水产科学, 2020, 27(11): 1379−1392. Wang Liang, Hua Chuanxiang, Zhu Qingcheng, et al. Review on the response of small pelagic fishery resources in the North Pacific to climate-ocean changes[J]. Journal of Fishery Sciences of China, 2020, 27(11): 1379−1392.
[24]	官文江, 朱江峰, 高峰. 印度洋长鳍金枪鱼资源评估的影响因素分析[J]. 中国水产科学, 2018, 25(5): 1102−1114. doi: 10.3724/SP.J.1118.2018.17303 Guan Wenjiang, Zhu Jiangfeng, Gao Feng. Analysis of influencing factors on stock assessment of the Indian Ocean albacore tuna (Thunnus alalunga)[J]. Journal of Fishery Sciences of China, 2018, 25(5): 1102−1114. doi: 10.3724/SP.J.1118.2018.17303
[25]	肖启华, 黄硕琳. 气候变化对海洋渔业资源的影响[J]. 水产学报, 2016, 40(7): 1089−1098. Xiao Qihua, Huang Shuolin. Climate change implications for marine fishery resources[J]. Journal of Fisheries of China, 2016, 40(7): 1089−1098.
[26]	Lehodey P, Alheit J, Barange M, et al. Climate variability, fish, and fisheries[J]. Journal of Climate, 2006, 19(20): 5009−5030. doi: 10.1175/JCLI3898.1
[27]	方海, 张衡, 刘峰, 等. 气候变化对世界主要渔业资源波动影响的研究进展[J]. 海洋渔业, 2008, 30(4): 363−370. Fang Hai, Zhang Heng, Liu Feng, et al. A summary of research progress related with the fluctuation of the worldwide main marine fishery resources influenced by climate changes[J]. Marine Fisheries, 2008, 30(4): 363−370.
[28]	Hampton J, Lewis A, Williams P. The western and central Pacific tuna fishery: overview and status of stocks[M]//SPC. Oceanic Fisheries Programme. New Caledonia: Secretariat of the Pacific Community, 1999.
[29]	Hampton J. Estimates of tag-reporting and tag-shedding rates in a large-scale tuna tagging experiment in the western tropical Pacific Ocean[J]. Oceanographic Literature Review, 1997, 95(1): 68−79.
[30]	Bakun A, Parrish R H. Comparative studies of coastal pelagic fish reproductive habitats: the Brazilian sardine (Sardinella aurita)[J]. ICES Journal of Marine Science, 1990, 46(3): 269−283. doi: 10.1093/icesjms/46.3.269
[31]	Deary A L, Moret-Ferguson S, Engels M, et al. Influence of Central Pacific oceanographic conditions on the potential vertical habitat of four tropical tuna species[J]. Pacific Science, 2015, 69(4): 461−475. doi: 10.2984/69.4.3
[32]	Ernesto T O, Arturo M M, Armando T, et al. Variation in yellowfin tuna (Thunnus albacares) catches related to El Niño-Southern Oscillation events at the entrance to the Gulf of California[J]. Fishery Bulletin, 2006, 104(2): 197−203.
[33]	周为峰, 陈亮亮, 崔雪森, 等. 异常气候下温跃层及时空因子对中西太平洋黄鳍金枪鱼渔场分布的影响[J]. 中国农业科技导报, 2021, 23(10): 192−201. Zhou Weifeng, Chen Liangliang, Cui Xuesen, et al. Effects of thermocline and space-time factors on Yellowfin Tuna fishing ground distribution in the central and westernpacific in abnormal climate[J]. Journal of Agricultural Science and Technology, 2021,23(10):192−201.
[34]	丁鹏, 邹晓荣, 丁淑仪, 等. 基于CNN-BiLSTM模型的黄鳍金枪鱼渔获量与气候因子关系研究[J]. 南方水产科学, 2024, 20(2): 19−26. Ding Peng, Zou Xiaorong, Ding Shuyi, et al. Study on relationship between Thunnus albacares catches and climatic factors based on CNN-BiLSTM model[J]. South China Fisheries Science, 2024, 20(2): 19−26.
[35]	徐策, 张力, 余静, 等. 气候变化对中国近海捕捞渔业的影响——以太平洋年代际涛动为例[J]. 资源科学, 2022, 44(2): 386−400. doi: 10.18402/resci.2022.02.14 Xu Ce, Zhang Li, Yu Jing, et al. Impact of climate change on China’s offshore fishing: taking the Pacific Decadal Oscillation as an example[J]. Resources Science, 2022, 44(2): 386−400. doi: 10.18402/resci.2022.02.14
[36]	Wu Lixin, Liu Zhengyu, Liu Yun, et al. Potential global climatic impacts of the North Pacific Ocean[J]. Geophysical Research Letters, 2005, 32(24): L24710.
[37]	张嘉容, 杨晓明, 戴小杰, 等. 南太平洋长鳍金枪鱼延绳钓渔获率与环境因子的关系研究[J]. 南方水产科学, 2020, 16(1): 69−77. doi: 10.12131/20190178 Zhang Jiarong, Yang Xiaoming, Dai Xiaojie, et al. Relationship between catch rate of longline albacore (Thunnus alalunga) and environmental factors in South Pacific[J]. South China Fisheries Science, 2020, 16(1): 69−77. doi: 10.12131/20190178
[38]	闫敏, 张衡, 樊伟, 等. 南太平洋长鳍金枪鱼渔场CPUE时空分布及其与关键海洋环境因子的关系[J]. 生态学杂志, 2015, 34(11): 3191−3197. Yan Min, Zhang Heng, Fan Wei, et al. Spatial-temporal CPUE profiles of the albacore tuna (Thunnus alalunga) and their relations to marine environmental factors in the South Pacific Ocean[J]. Chinese Journal of Ecology, 2015, 34(11): 3191−3197.