# Machine Learning Reconstruction of Wyrtki Jet Seasonal Variability in the Equatorial Indian Ocean
## 1 Introduction
Combining the altimeter-derived currents with SST information, they created daily, gap-free, high-resolution maps of sea surface currents for the period 2012-2016. On a global scale, [PERSON] et al. [29] established the maximum likelihood estimation (MLE) method to retrieve the ocean surface current and wind simultaneously. [PERSON] et al. [30] employed a global isotropic hexagonal grid derived from satellite remote sensing data to estimate global ocean surface currents. Notably, compared to other ocean basins (e.g., Pacific and Atlantic), research on sea current velocity reconstruction using machine learning (ML) approaches remains relatively scarce in the Indian Ocean basin. Current investigations in this area predominantly focus on reconstructing other marine environmental parameters, including subsurface temperature anomalies [31; 32], subsurface temperature [33], sea surface temperature [34; 35; 36; 37; 38], thermocline water temperature [37], sea surface temperature gradient [39], sea surface salinity (SSS) [40], subsurface salinity [41; 42], sea level [43], dissolved oxygen [44], fisheries catches [45; 46], chlorophyll-a variations [47], three-dimensional temperature and salinity profiles [20], sea-surface conditions [48; 49], three-dimensional eddy structures [50], standardized precipitation evapotranspiration index [51], nitrate concentration [52], seawater temperature and precipitation changes [53; 54], seawater oxygen isotope [38], mixed layer depth (MLD) [55], and barrier layer thickness [56]. However, the rapid advancement of ocean satellite remote sensing technologies and machine learning methodologies has enabled the potential application of artificial intelligence techniques for reconstructing the WJ in the equatorial Indian Ocean, demonstrating the following distinctive advantages:  
1. Machine learning algorithms (e.g., Extreme Gradient Boosting (XGBoost)) can capture complex nonlinear relationships between sea surface parameters (e.g., SST, SSS, sea level anomaly (SLA)) and WJ velocity, overcoming limitations inherent to conventional linear regression approaches. 2. Through the integration of satellite-derived parameters (e.g., wind stress components (Tx and Ty); wind stress curl (Curlz)) and geographical information (longitude, latitude), ML models can comprehensively characterize the spatial distribution patterns of WJ. 3. Compared with numerical models, trained ML algorithms enable rapid reconstruction with significantly reduced computational costs. This study employed two state-of-the-art ML algorithms (XGBoost), combined with multi-source satellite observations--including SST, SSS, SLA, Curlz, Tx, Ty, 20 \({}^{\circ}\)C isotherm depth (D20), and geographical coordinates (longitude and latitude), to reconstruct the seasonal variability of WJ in the equatorial Indian Ocean. The results demonstrated that the XGBoost-based approach effectively captures both spatial distribution patterns and seasonal evolution characteristics of WJ, exhibiting high reconstruction accuracy (\(R^{2}>0.85\)) and reliability (RMSE \(<0.07\) m/s). This research not only addresses a critical technical gap in Indian Ocean current reconstruction studies but also provides novel methodological support for future investigations of Indian Ocean circulation dynamics and air-sea interactions. The rest of the paper is organized as follows. Section 2 describes the data and methods. Section 3 evaluates the model performance in estimating seasonal variations of WJ in the equatorial Indian Ocean. Finally, Section 4 is the discussion, and Section 5 provides conclusions.