文献类型：期刊文献

英文题名：Machine Learning Reconstruction of Wyrtki Jet Seasonal Variability in the Equatorial Indian Ocean

作者：Li, Dandan[1];Zheng, Shaojun[1,2,3];Zheng, Chenyu[1];Xie, Lingling[1,2,3];Yan, Li[1,2,3]

机构：[1]Guangdong Ocean Univ, Coll Ocean & Meteorol, Lab Coastal Ocean Variat & Disaster Predict, Zhanjiang 524088, Peoples R China;[2]Guangdong Ocean Univ, Key Lab Climate Resources & Environm Continental S, Dept Educ Guangdong Prov, Zhanjiang 524088, Peoples R China;[3]Minist Nat Resources, Key Lab Space Ocean Remote Sensing & Applicat, Beijing 100081, Peoples R China

年份：2025

卷号：18

期号：7

外文期刊名：ALGORITHMS

收录：EI(收录号：20253018866908)、ESCI(收录号：WOS:001539549700001)、Scopus(收录号：2-s2.0-105011634996)、WOS

基金：This study was supported by the Innovative Team Plan for Department of Education of Guangdong Province (2023KCXTD015), 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).

语种：英文

外文关键词：equatorial Indian Ocean; Wyrtki Jet; seasonal variability; machine learning; XGBoost

外文摘要：The Wyrtki Jet (WJ), a pivotal surface circulation system in the equatorial Indian Ocean, exerts significant regulatory control over regional climate dynamics through its intense eastward transport characteristics, which modulate water mass exchange, thermohaline balance, and cross-basin energy transfer. To address the scarcity of in situ observational data, this study developed a satellite remote sensing-driven multi-parameter coupled model and reconstructed the WJ's seasonal variations using the XGBoost machine learning algorithm. The results revealed that wind stress components, sea surface temperature, and wind stress curl serve as the primary drivers of its seasonal dynamics. The XGBoost model demonstrated superior performance in reconstructing WJ's seasonal variations, achieving coefficients of determination (R2) exceeding 0.97 across all seasons and maintaining root mean square errors (RMSE) below 0.2 m/s across all seasons. The reconstructed currents exhibited strong consistency with the Ocean Surface Current Analysis Real-time (OSCAR) dataset, showing errors below 0.05 m/s in spring and autumn and under 0.1 m/s in summer and winter. The proposed multi-feature integrated modeling framework delivers a high spatiotemporal resolution analytical tool for tropical Indian Ocean circulation dynamics research, while simultaneously establishing critical data infrastructure to decode monsoon current coupling mechanisms, advancing early warning systems for extreme climatic events, and optimizing regional marine resource governance.