## 4 Discussion  
The ELM was the most effective (\(ACC=86.92\%\)) in forecasting the South Pacific alba-core fishing grounds, with an overall improvement of 4.39-19.48% over the machine learning model. This study found that the ELM can integrate learning based on the output results of the base model as the input features of the second-layer meta-learner (LR) model, thus better integrating the advantages of the base model and producing more stable results. Meanwhile, different base models were 5-fold cross-validated, avoiding the problem of the poor generalization ability of test data division, effectively suppressing model over-fitting and improving the generalization ability and forecast accuracy of the ELM, similar to the findings of [PERSON] et al. (2021), [PERSON] and [PERSON] (2023), and [PERSON] et al. (2021) [20,50,51]. The XGBoost model, as a single machine learning algorithm, was better in forecasting albacore fishing grounds (\(ACC=82.53\%\)), where its overall accuracy was 1.58-15.09% better than that of other machine learning algorithms, and its accuracy (\(R_{2}\)) in forecasting high-yield tuna fishing grounds reached 90.37%. It was found that, compared to other machine learning algorithms, the XGBoost model can effectively prevent overfitting, thus enhancing the generalization of the model and improving the accuracy of the model and the efficiency of the algorithm, similar to the findings of [PERSON] et al. (2022) and [PERSON] et al. (2021) [52,53]. The GP model was the least effective in forecasting different grades of fishing grounds, where its \(ACC\) was lower than that of other machine learning models (10.29-15.09%), and the difference in the KNN model's forecast accuracy for low- and high-yield fishing grounds was large (\(R_{1}=60.85\%\), \(R_{2}=86.16\%\)). This paper found that different machine learning models have certain limitations. For example, GP models are less capable of handling high-dimensional data and have high computational complexity, thus leading to performance degradation, similar to the findings of [PERSON] et al. (2005) [45]. KNN models are prone to classification bias when the category distribution is uneven, which may affect the model performance, consistent with the findings of [PERSON] et al. (1967) [54]. In addition, the results of this study showed that the recall and precision of different algorithms in forecasting high-yield fishing grounds of albacore were better than those of low-yield fishing grounds. The marine environment is complex and diverse, and trends in albacore activity are closely related to the marine environment [33,38,55]. It was found that, in January-February and October-December, there were few catches south of 30\({}^{\circ}\) S, and most of the high-yield fishing grounds were concentrated in 10\({}^{\circ}\) S-25\({}^{\circ}\) S waters, while in March and April, the distribution of fishing grounds was more scattered, but, in May-August, the albacore fishing grounds were more concentrated, and the high-yield fishing grounds were mainly distributed in 10\({}^{\circ}\) S-40\({}^{\circ}\) S waters. Additionally, this study found that most of the low-yield fishing grounds of albacore were distributed between 0\({}^{\circ}\) and 10\({}^{\circ}\) S, similar to the findings of [PERSON] (1985) [56]. Most of the high-yield fishing grounds were distributed in waters from 10\({}^{\circ}\) S to 40\({}^{\circ}\) S, showing a gradual southward expansion from January, migrating southward in early summer, and beginning to migrate northward in winter, with a clear seasonal trend in longline catches, consistent with the findings of [PERSON] et al. (2015) [57]. It has been shown that Lat is the most important marine environmental variable for forecasting different grades of albacore fishing grounds, and it has been widely used by many scholars for forecasting fishing grounds of different marine fishes [58,59].