## 3 Results  
### Temporal Changes in Environment Variables  
Figure 5 shows the variation in different marine environmental factors from January to December 2009-2019. In the whole time series, the value of marine environmental variable Chl-a reached its highest value in August 2012 (Chl-a = 0.172 mg/m\({}^{3}\)) and its lowest value in April 2016 (0.095 mg/m\({}^{3}\)). Furthermore, the value of Chl-a was at a low level in February-April in different years and then gradually elevated to reach higher levels in July-September, but in 2019, the highest values of Chl-a were reached in both February and July. The marine environmental variable SSS showed a decreasing, increasing and decreasing trend in the overall time series, with the highest value of SSS in October 2011 (35.51 psu) and the lowest value in March 2019 (35.04 psu). Meanwhile, the SSS reached a lower value in February-April in different years, with its peak mostly occurring in August-November of each year, but the variation in the values of SSS was lower in the months of June-August in 2012, 2016 and 2019. SST showed a decreasing and then increasing trend in the overall time series, where it reached its highest value in February 2016 (29.35 \({}^{\circ}\)C) and dropped to its lowest in July 2018 (24.55 \({}^{\circ}\)C). In different years, the value of SST was at a low level in May-August each year, while its peak occurred in January-March and September-December. The marine environmental variable SSH showed a chaotic trend in the overall time series, with no obvious change pattern, reaching its lowest value in June 2009 and 2017, with only 0.564 m in June 2017, while the peak of SSH appeared in October and December 2018, where it reached 0.726 m in December. A comparative Analysis of the Accuracy of Machine Learning Algorithms and Ensemble Learning Model for Albacore Fishing Grounds Forecasting  
To investigate the potential of ELM for forecasting different grades of albacore fishing grounds, this paper constructed ELM based on RF, SVM, KNN, GP and XGBoost algorithms for forecasting albacore fishing grounds, and the forecasting results of each model are shown in Table 2. The ELM achieved high accuracy forecasting of albacore fishing grounds (\(ACC=86.92\%\)), an overall improvement of 4.39\(\sim\)19.48% over the machine learning model (Table 2). The average accuracy of different models for albacore fishing ground forecasting over 12 months is shown in Table 2, among which the XGBoost model obtained better fishing ground forecasting results (Figure 6). The average overall accuracy (\(ACC\)) for fishing ground \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model Accuracy Evaluation** & **RF** & **KNN** & **SVM** & **GP** & **XGBoost** & **ELM** \\ \hline Overall Accuracy: ACC & 80.95\% & 77.73\% & 79.37\% & 67.44\% & 82.53\% & 86.92\% \\ Low-yield average recall: R\({}_{1}\) & 65.13\% & 60.85\% & 63.07\% & 58.41\% & 66.86\% & 77.00\% \\ High-yield average recall: R\({}_{2}\) & 88.82\% & 86.16\% & 87.47\% & 71.95\% & 90.37\% & 91.85\% \\ Low-yield average precision: P\({}_{1}\) & 74.23\% & 68.51\% & 71.32\% & 51.50\% & 77.20\% & 80.40\% \\ High-yield average precision: P\({}_{2}\) & 84.16\% & 81.93\% & 83.10\% & 77.85\% & 85.01\% & 89.93\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The average accuracy of different models in forecasting different grades of albacore fishing grounds in 2018–2019. Figure 5: Changes in different environmental factors by month from 2009 to 2019.  
forecasting over 12 months was 82.53%, the average recall for low-yield fishing grounds (\(R_{1}\)) was 66.86%, the average precision (\(P_{1}\)) was 77.20% and each model evaluation index was the highest.