The relationship between the annual catch of bigeye tuna and climate factors and its prediction
IntroductionIn order to explore the impact of climate factors on bigeye tuna catch, monthly data of nine climate factors, including El Niño-related indices (Niño1 + 2, Niño3, Niño4, and Niño3.4), Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO),...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2024.1344966/full |
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| author | Peng Ding Hui Xu Xiaorong Zou Xiaorong Zou Xiaorong Zou Shuyi Ding Siqi Bai |
| author_facet | Peng Ding Hui Xu Xiaorong Zou Xiaorong Zou Xiaorong Zou Shuyi Ding Siqi Bai |
| author_sort | Peng Ding |
| collection | DOAJ |
| description | IntroductionIn order to explore the impact of climate factors on bigeye tuna catch, monthly data of nine climate factors, including El Niño-related indices (Niño1 + 2, Niño3, Niño4, and Niño3.4), Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), North Pacific Index (NPI), and global sea–air temperature anomaly index (dT), were combined with the annual data of global bigeye tuna catch.MethodsThe relationship between low-frequency climate factors and bigeye tuna catch was studied using long short-term memory(LSTM) model, random forest (RF) model, BP neural network model, extreme gradient boosting tree (XGBoost) model, and Sparrow search optimization algorithm extreme gradient boosting tree (SSA-XGBoost) model.ResultsThe results show that the optimal lag periods corresponding to the climate change characterization factors Niño1 + 2, dT, SOI, NPI, NAO, and PDO are 15 years,12 years, 12 years, 1 year, 14 years, and 4 years, respectively. The SSA-XGBoost model have the highest prediction accuracy, followed by XGBoost, BP, LSTM, and RF. The fitting degree between the predicted values and the actual values of the SSA-XGBoost model is 0.853, the mean absolute error is 0.104, the root mean square error is 0.124.DiscussionThe trend between the predicted values and the actual values of the SSA-XGBoost model is generally consistent, indicating good model fitting performance, which can provide a basis for the management of bigeye tuna fisheries. |
| format | Article |
| id | doaj-art-0a8cd819b2364c41bd95f4cd699c2f29 |
| institution | OA Journals |
| issn | 2296-7745 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj-art-0a8cd819b2364c41bd95f4cd699c2f292025-08-20T02:32:12ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452024-12-011110.3389/fmars.2024.13449661344966The relationship between the annual catch of bigeye tuna and climate factors and its predictionPeng Ding0Hui Xu1Xiaorong Zou2Xiaorong Zou3Xiaorong Zou4Shuyi Ding5Siqi Bai6College of Marine Living Resource Sciences and Management, Shanghai Ocean University, Shanghai, ChinaGraduate School of Fisheries Sciences, Hokkaido University, Hakodate, JapanCollege of Marine Living Resource Sciences and Management, Shanghai Ocean University, Shanghai, ChinaCollaborative Innovation Center for National Distant-water Fisheries, Shanghai Ocean University, Shanghai, ChinaKey Laboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education, Shanghai, ChinaSchool of Education, Shandong Women’s University, Jinan, ChinaCollege of Marine Living Resource Sciences and Management, Shanghai Ocean University, Shanghai, ChinaIntroductionIn order to explore the impact of climate factors on bigeye tuna catch, monthly data of nine climate factors, including El Niño-related indices (Niño1 + 2, Niño3, Niño4, and Niño3.4), Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), North Pacific Index (NPI), and global sea–air temperature anomaly index (dT), were combined with the annual data of global bigeye tuna catch.MethodsThe relationship between low-frequency climate factors and bigeye tuna catch was studied using long short-term memory(LSTM) model, random forest (RF) model, BP neural network model, extreme gradient boosting tree (XGBoost) model, and Sparrow search optimization algorithm extreme gradient boosting tree (SSA-XGBoost) model.ResultsThe results show that the optimal lag periods corresponding to the climate change characterization factors Niño1 + 2, dT, SOI, NPI, NAO, and PDO are 15 years,12 years, 12 years, 1 year, 14 years, and 4 years, respectively. The SSA-XGBoost model have the highest prediction accuracy, followed by XGBoost, BP, LSTM, and RF. The fitting degree between the predicted values and the actual values of the SSA-XGBoost model is 0.853, the mean absolute error is 0.104, the root mean square error is 0.124.DiscussionThe trend between the predicted values and the actual values of the SSA-XGBoost model is generally consistent, indicating good model fitting performance, which can provide a basis for the management of bigeye tuna fisheries.https://www.frontiersin.org/articles/10.3389/fmars.2024.1344966/fullclimate factorsbigeye tuna catchmachine learning modelpredictionSSA-XGBoost model |
| spellingShingle | Peng Ding Hui Xu Xiaorong Zou Xiaorong Zou Xiaorong Zou Shuyi Ding Siqi Bai The relationship between the annual catch of bigeye tuna and climate factors and its prediction Frontiers in Marine Science climate factors bigeye tuna catch machine learning model prediction SSA-XGBoost model |
| title | The relationship between the annual catch of bigeye tuna and climate factors and its prediction |
| title_full | The relationship between the annual catch of bigeye tuna and climate factors and its prediction |
| title_fullStr | The relationship between the annual catch of bigeye tuna and climate factors and its prediction |
| title_full_unstemmed | The relationship between the annual catch of bigeye tuna and climate factors and its prediction |
| title_short | The relationship between the annual catch of bigeye tuna and climate factors and its prediction |
| title_sort | relationship between the annual catch of bigeye tuna and climate factors and its prediction |
| topic | climate factors bigeye tuna catch machine learning model prediction SSA-XGBoost model |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2024.1344966/full |
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