# Machine Learning Reconstruction of Wyrtki Jet Seasonal Variability in the Equatorial Indian Ocean
## 3 Results
### Evaluation of the XGBoost Model
To thoroughly evaluate the performance of the XGBoost model in reconstructing seasonal variations of the WJ zonal velocity in the equatorial Indian Ocean, the spatial distribution of WJ zonal velocity from OSCAR and XGBoost model in 2022 is shown in Figure 8. The observational results revealed pronounced seasonal variability in WJ zonal velocity: the velocity peaks at approximately 0.6 m/s in November (Figure 8g), corresponding to the intensified phase of the autumn WJ, while the May peak (Figure 8c) reaches approximately 0.4 m/s, aligning with the active period of the spring WJ. Additionally, the WJ exhibited stable westward flow in February (Figure 8a) and weaker eastward flow in August (Figure 8e), which is closely linked to the seasonal transition of the Indian Ocean monsoon. In model performance comparisons, the XGBoost-reconstructed spatial distributions of WJ zonal velocity (Figure 8b,d,f,h) showed high consistency with OSCAR observations (Figure 8a,c,e,g), particularly in February, May, August, and November. The XGBoost model effectively captured both the high-velocity core regions and spatial extents of WJ. To further quantify the discrepancies between model reconstructions and observational data, Figure 9 presents the error distributions of the XGBoost-reconstructed WJ zonal velocity relative to the OSCAR dataset. The results demonstrated that the XGBoost-reconstructed velocity exhibited relatively smaller errors (Figure 9a-d), In February, May, and November, regions with an error of 0.2 m/s were mainly in 50\({}^{\circ}\) E-60\({}^{\circ}\) E, and other regions had an error of about 0.05 m/s. In August, the regions with an error of 0.2 m/s shifted to 60\({}^{\circ}\) E-70\({}^{\circ}\) E, and other regions still had a low error of about 0.05 m/s. Comprehensive analysis revealed that both models effectively captured the temporal variability of WJ zonal velocity, and the XGBoost model demonstrated superior overall accuracy and stability, consistently maintaining lower reconstruction errors across all months. To quantitatively assess the XGBoost model's reconstruction of the WJ zonal velocity, scatter plots comparing the model output with OSCAR observational data are presented for February (Figure 10a), May (Figure 10b), August (Figure 10c), and November (Figure 10d). Evaluation using \(R^{2}\) and \(RMSE\) metrics revealed significant seasonal variations in reconstruction accuracy. The model demonstrated optimal performance in February (Figure 10a; \(R^{2}\) = 0.97, \(RMSE\) = 0.03 m/s), precisely capturing the WJ velocity dynamics. High perfor  
Figure 8: Seasonal distributions of the Wyrtki jet in the tropical Indian Ocean in 2022: (**a**) February, (**c**) May, (**e**) August, and (**g**) November from the OSCAR dataset; XGBoost model-derived estimations for (**b**) February, (**d**) May, (**f**) August, and (**h**) November. mance was also evident in May (Figure 10b; \(R^{2}\) = 0.96, \(RMSE\) = 0.04 m/s) and November (Figure 10d; \(R^{2}\) = 0.95, \(RMSE\) = 0.05 m/s). While August (Figure 10c) showed a moderate reduction in correlation (\(R^{2}\) = 0.90, \(RMSE\) = 0.07 m/s), performance remained statistically robust. Following the July peak, the WJ started to weaken rapidly and nearly vanished. These results confirmed the high reliability of the XGBoost model in reconstructing WJ zonal velocity and its capacity to characterize seasonal variability within the study domain, underscoring its substantial applicability for simulating WJ dynamics in the equatorial Indian Ocean. Figure 11 illustrates the seasonal variability of the WJ zonal velocity within the study region (50\({}^{\circ}\) E-80\({}^{\circ}\) E, 3\({}^{\circ}\) S-3\({}^{\circ}\) N) during 2022, comparing observational data from the OSCAR dataset with reconstruction results obtained from the XGBoost model. The OSCAR observations demonstrated distinct seasonal dynamics, with the WJ zonal velocity peaking at 0.35 m/s in November, followed by 0.31 m/s in May.