# Machine Learning Reconstruction of Wyrtki Jet Seasonal Variability in the Equatorial Indian Ocean
## 5 Conclusions  
This study investigated the seasonal variability of the WJ in the equatorial Indian Ocean using satellite observations and machine learning models (XGBoost). The results demonstrated that wind stress components (Tx and Ty), SST, and wind stress curl were the dominant factors influencing the seasonal variability of the WJ. The XGBoost model outperformed LightGBM in reconstructing the WJ and its seasonal variations, achieving \(R^{2}\) values exceeding 0.97 across all seasons and maintaining root mean square errors (_RMSE_) below 0.2 m/s across all seasons. The discrepancies between the XGBoost-reconstructed zonal velocity and the OSCAR dataset were notably small, with errors below 0.05 m/s in summer and winter and below 0.1 m/s in spring and autumn, confirming its robustness and reliability. The study also revealed significant seasonal variations in the velocity of the Wyrtki Jet in the equatorial Indian Ocean: the maximum velocity occurs in autumn (approximately 0.5 m/s), followed by spring (around 0.3 m/s), with summer velocities averaging 0.11 m/s and winter reaching the minimum (about \(-\)0.21 m/s). The XGBoost model demonstrated a significantly positive correlation between its estimated Wyrtki Jet velocities and the zonal velocity (U) derived from OSCAR data, validating its accuracy and reliability in capturing the seasonal variations of the jet's zonal flow. These results highlight the superior performance of the XGBoost model in zonal velocity inversion for the equatorial Indian Ocean Wyrtki Jet, providing precise data support for research on regional ocean circulation dy  
Figure 12: Seasonal variability of Wyrtki Jet zonal velocity in the region of 50\({}^{\circ}\) E–80\({}^{\circ}\) E, 3\({}^{\circ}\) S–3\({}^{\circ}\) N from the OSCAR dataset (magenta line) and the XGBoost model reconstruction in 2013 (blue line). namics and effectively addressing the observational gaps in current velocity measurements for this critical current system. The XGBoost model stands out for its streamlined architecture, which directly integrates multiple parameters to accurately extract feature information from variables and comprehensively account for their interactions. However, as a data-driven model, it exhibits inherent limitations, such as tendencies toward overestimation or underestimation. Notably, the zonal velocity of the Wyrtki Jet is also influenced by ocean dynamic and thermodynamic processes. Future research will incorporate additional marine dynamic mechanisms to enhance the estimation precision of Wyrtki Jet zonal flow velocities. The multi-source feature fusion modeling framework proposed in this study provides a high spatial temporal-resolution tool for investigating tropical Indian Ocean circulation dynamics, offering critical data support for analyzing monsoon-ocean current coupling mechanisms, early warning of extreme climate events, and regional resource management. D.L. : data analysis; writing--original draft; S.Z. : study design; writing--review and editing. C.Z. : concepts discussed; writing--review and editing; L.X. and L.Y. : concepts discussed; suggestions provided. All authors have read and agreed to the published version of the manuscript. This study was supported by the Innovative Team Plan for Department of Education of Guangdong Province (2023 KCXTD015), the First-class Discipline Plan of Guangdong Province (080508032401, 010202032401), and the program for scientific research start-up funds of Guangdong Ocean University (R19061, 060302032104). The datasets presented in this study are publicly available. The monthly wind data were obtained from [[https://cds.climate.copernicus.eu/cdsapp##](https://cds.climate.copernicus.eu/cdsapp##)]([https://cds.climate.copernicus.eu/cdsapp##](https://cds.climate.copernicus.eu/cdsapp##))!/dataset/reanalysis-era5-single-levels-monthly-means?tab=overview (accessed on 10 June 2023).