## 3 Results
### Temporal Changes in Environment Variables
The forecast accuracy (\(ACC\)) of albacore fishing grounds was improved by 1.58-15.09% compared to other machine learning algorithms, especially for high-yield fishing grounds, where its average recall (\(R_{2}\)) and average precision (\(P_{2}\)) reached 90.37% and 85.01%, respectively (Table 2), proving the high accuracy of the XGBoost model for high-yield fishing ground forecasting of albacore. The RF model also achieved an average fishing ground forecast accuracy (\(ACC\)) of 80.95% for albacore over 12 months, second only to the XGBoost model, and an overall improvement of 1.58-13.51% in \(ACC\) over other machine learning algorithms. For low-yield fishing grounds, its average recall (\(R_{1}\)) was 65.13%, and its average precision (\(P_{1}\)) was 74.23%, while for high-yield fishing grounds, its average recall (\(R_{2}\)) was 88.82%, and its average precision (\(P_{2}\)) was 84.16%. The effect of the GP model on albacore fishing grounds prediction was poor (\(ACC\) = 67.44%), as shown in Figure 6, and all accuracy indicators of the model were lower than those of other algorithms. For low-yield fishing grounds, its average precision (\(P_{1}\)) was only 51.50%, and its average recall (\(R_{1}\)) was 58.41%, while its overall accuracy (\(ACC\)) in forecasting the fishing grounds of albacore was lower than that of other algorithms by 10.29-15.09% (Table 2). The reason for this finding may be because the GP model is less sensitive to the missing data in the dataset, the samples in the study area are unevenly distributed, the resource abundance is more scattered and there are no fishing data of albacore in some areas, leading to errors in the GP model forecasts. Figure 6: Comparison and analysis of month-by-month forecast accuracy distribution and average accuracy of different models for different grades of albacore fishing grounds in 2018–2019. Figure 6 compares the forecast accuracy of albacore fishing grounds for each month of 2018-2019 for each model, where the black points represent the forecast accuracy for each month and the red points are the mean values. The ELM returned the best forecast for albacore fishing grounds (Figure 6), and the evaluation indexes of each model were at a high level, with an average overall accuracy (\(ACC\)) of 86.92% for different months of the fishing ground forecast, along with an average recall (\(R_{2}\)) and precision (\(P_{2}\)) of 91.85% and 89.93% for high-yield fishing grounds, which were 1.48-19.90% and 4.92-12.08%, respectively--better than those of the machine learning algorithm overall (Table 2). The average recall and accuracy of the low-yield fishing grounds outperformed those of the machine learning model. Compared to RF, KNN, SVM, GP and XGBoost, the overall accuracy (\(ACC\)) of the ELM for the average fishing ground forecast of albacore in different months was improved by 5.97%, 9.19%, 7.55%, 19.48% and 4.39%, respectively, as shown in Table 2. The high-yield average recall \(R_{2}\) improved by 3.03%, 5.69%, 4.38%, 19.90% and 1.48% compared to the RF, KNN, SVM, GP and XGBoost algorithms, respectively, with the most significant improvement compared to the GP model (19.90%) and a smaller improvement compared to XGBoost (1.48%). Relative to GP, the ELM had the most significant improvement in the average precision (\(P_{1}\)) of the low-yield albacore fishing ground forecasts (28.90%), with 12.08% for the high-yield fishing ground forecasts (\(P_{2}\)). Relative to the XGBoost model, the ELM also improved the average recall (\(R_{1}\)) and precision (\(P_{1}\)) of the low-yield albacore fishing ground forecasts by 10.14% and 3.20%. This fully illustrates the stability and applicability of the ELM for forecasts of different grades of albacore fishing grounds, and the low applicability of the GP model for albacore fishing ground forecasts.