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
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- 文章访问数: 87
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- 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

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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

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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

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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

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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

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图 6 气候变化表征因子敏感性分析

Fig. 6 Sensitivity analysis of climate change characterization factors

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