# Machine Learning Reconstruction of Wyrtki Jet Seasonal Variability in the Equatorial Indian Ocean
## 3 Results
### Evaluation of the XGBoost Model  
In order to comprehensively evaluate the performance of the XGBoost model in reconstructing the seasonal variability of the WJ, we developed the XGBoost framework to estimate the zonal velocity of the WJ in the equatorial Indian Ocean. Figure 5 presents the horizontal distributions of the WJ derived from OSCAR data during spring (Figure 5a), summer (Figure 5c), autumn (Figure 5e), and winter (Figure 5g) of 2022. Notably, the WJ peaks in autumn (approximately 0.6 m/s), significantly exceeding spring velocity (\(-0.3\) m/s), demonstrating pronounced seasonal disparities. These results align with previous studies [1; 7; 8], further corroborating the seasonal variability of WJ in this region. The spatial distributions of WJ velocity estimated by the XGBoost model (Figure 5b,d,f,h) exhibited superior consistency with the zonal velocity distributions from OSCAR data (Figure 5a,c,e,g), confirming the model's capability of accurately reconstructing both the spatial patterns and seasonal dynamics of the WJ. Therefore, the XGBoost model could be used for seasonal variability reconstruction of the WJ. To comprehensively evaluate the performance of the XGBoost model in reconstructing the WJ, we analyzed the spatial distributions of discrepancies between the WJ velocity derived from both machine learning models and those from the OSCAR dataset (Figure 6). The results demonstrated that the differences between XGBoost-reconstructed and OSCAR-based WJ velocity were generally smaller, particularly during spring (Figure 6a) and autumn (Figure 6c), in which errors remained below 0.05 m/s. Although slightly higher discrepancies were observed in summer (Figure 6b) and winter (Figure 6d), the overall errors were still constrained to less than 0.1 m/s. Consequently, the XGBoost model demonstrated superior performance in reconstructing the Wyrtki jet in the equatorial Indian Ocean, establishing its efficacy for reconstructing regional oceanic circulation dynamics. Figure 5: Seasonal distributions of the Wyrtki jet in the equatorial Indian Ocean for 2022: (**a**) spring, (**c**) summer, (**e**) autumn, and (**g**) winter from the OSCAR dataset; (**b**) spring, (**d**) summer, (**f**) autumn, and (**h**) winter from the XGBoost model estimates. To further investigate the reconstruction performance of the XGBoost model for the Wyrtki Jet within the study region (50\({}^{\circ}\) E-90\({}^{\circ}\) E, 5\({}^{\circ}\) S-5\({}^{\circ}\) N), Figure 7 presents seasonal scatterplots comparing the XGBoost-reconstructed jet velocity with the corresponding OSCAR dataset during spring (Figure 7a), summer (Figure 7b), autumn (Figure 7c), and winter (Figure 7d). The data points were mainly concentrated near the contour lines, indicating strong agreement between model reconstructions and observational data. To quantify this consistency, \(R^{2}\) and \(RMSE\) were employed as the evaluation metrics. The results demonstrated that the XGBoost-reconstructed WJ velocity exhibited strong agreement with OSCAR-derived data across all seasons, achieving consistent \(R^{2}\) values of 0.97 and \(RMSE\leq 0.2\) and confirming the model's robust capability to capture seasonal variations. This robust performance highlights the effectiveness and reliability of the XGBoost model in reconstructing oceanic zonal current dynamics, solidifying its utility for high precision marine current reconstruction in the equatorial Indian Ocean. Figure 6: Discrepancies between model estimations and OSCAR for the Wyrtki Jet in the equatorial Indian Ocean during 2022: (**a**) spring, (**b**) summer, (**c**) autumn, and (**d**) winter derived from the XGBoost model. Figure 7: Scatter plots of the zonal velocity of Wyrtki Jet from the OSCAR dataset versus XGBoost inversion of the zonal velocity of Wyrtki Jet in (**a**) spring, (**b**) summer, (**c**) autumn, and (**d**) winter of 2022.