## 5 Conclusions  
To achieve high-accuracy forecasting of albacore fishing grounds in the South Pacific Ocean, to investigate the forecasting ability of different machine learning models for albacore fishing grounds, and to quantitatively evaluate the contribution of different marine environmental factors to fishing ground forecasting, this paper compared and analyzed the forecasting ability of the RF, KNN, SVM, XGBoost and GP machine learning algo-rithms for fishing grounds, and built an ELM based on machine learning algorithms to further improve the accuracy of fishing ground forecasting. The specific conclusions are as follows:  
1. The XGBoost model had a better forecast accuracy (\(ACC=82.53\%\)) for South Pacific albacore fishing grounds, which was 1.58-15.09% better than that of other machine learning models. The XGBoost and RF models have better application prospects for albacore fishing ground forecasting. The ELM was the best in forecasting albacore fishing grounds (\(ACC=86.92\%\)), and it improved the overall performance by 4.39-19.48% over the machine learning model. High-accuracy forecasting of albacore fishing grounds in the South Pacific was achieved.
2. The forecast accuracy of the high-yield South Pacific albacore fishing grounds based on the ELM was more stable, with the recall of high-yield fishing grounds exceeding 80% in all months, and the forecast accuracy of the recall of low-yield fishing grounds fluctuating widely. Most of the fishing ground forecast errors occurred at the junction of low-yield fishing grounds and high-yield fishing grounds, with fewer catches in the sea near the equator. Most of the high-yield fishing grounds were distributed in the sea south of 10\({}^{\circ}\) S, and there was a clear seasonal trend, the grounds migrating southward in early summer and beginning to migrate northward in winter.
3. Lat contributed the most to the forecast of South Pacific albacore fishing grounds in February-December, exceeding 0.224 in different months, while Chl-a had the highest importance to the forecast of albacore fishing grounds in January (0.295), and Lon had the smallest effect on the forecast of albacore fishing grounds in different months.  
Conceptualization, J.Z. and H.H.; methodology, J.Z.; software, D.F.; validation, B.X., Y.X. and J.S.; formal analysis, Y.X.; investigation, J.S.; resources, J.Z.; data curation, J.Z.; writing--original draft preparation, J.Z.; writing--review and editing, H.H.; visualization, D.F.; supervision, B.X.; project administration, H.H.; funding acquisition, H.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Basic Scientific Research Ability Improvement Project for Young and Middle-aged Teachers of Universities in Guangxi (2021 KY0255), the Natural Science Foundation of Guangxi Province (CN) (2022 GXNSFBA035637), and the 'Ba Gui Scholars' program of the provincial government of Guangxi. We thank the anonymous reviewers for their comments and suggestions, which helped to improve the quality of this manuscript.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Not applicable.  
We thank the anonymous reviewers for their comments and suggestions, which helped to improve the quality of this manuscript.  
The authors declare no conflict of interest, and the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.