首页|Researchers from Chinese Academy of Sciences Report Details of New Studies and Findings in the Area of Machine Learning (Advancing Ocean Subsurface Thermal Structure Estimation In the Pacific Ocean: a Multi-model Ensemble Machine Learning ...)
Researchers from Chinese Academy of Sciences Report Details of New Studies and Findings in the Area of Machine Learning (Advancing Ocean Subsurface Thermal Structure Estimation In the Pacific Ocean: a Multi-model Ensemble Machine Learning ...)
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NETL
NSTL
Elsevier
Investigators publish new report on Machine Learning. According to news reporting originating in Qingdao, People’s Republic of China, by NewsRx journalists, research stated, “Estimation of the ocean subsurface thermal structure (OSTS) is important for understanding thermodynamic processes and climate variability. In the present study, a novel multi-model ensemble machine learning (Ensemble- ML) model is developed to retrieve subsurface thermal structure in the Pacific Ocean by integrating sea surface data with Argo observations.” Funders for this research include National Key Research and Development Program of China, National Natural Science Foundation of China (NSFC). The news reporters obtained a quote from the research from the Chinese Academy of Sciences, “The Ensemble-ML model integrates four individual machine learning models to enhance estimation accuracy and reliability. Our results exhibit good agreement between the satellite sea surface temperature (SST) and sea surface salinity (SSS) data and Argo observations, providing validation for the utilization of these datasets in the Ensemble-ML model. The Ensemble-ML model exhibits better performance compared to individual machine learning models, with an average root mean square error (RMSE) of 0.3273 degrees C and an average coefficient of determination (R2) of 0.9905. Notably, incorporating geographical information as input variables enhance model performance, emphasizing the importance of considering spatial context in OSTS estimation. The Ensemble-ML model accurately captures the spatial distribution of OSTS across depths and seasons in the Pacific Ocean, effectively reproducing critical temperature features while maintaining strong agreement with Argo observations. Nevertheless, its performance shows relative weakness within the thermocline layer and the equatorial Pacific region (spanning from 10 degrees S to 10 degrees N latitude), which are characterized by complex circulation systems. Despite these challenges, the Ensemble-ML model effectively reproduces the spatial distribution of OSTS of the Pacific Ocean.”
QingdaoPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningChinese Academy of Sciences
NETL
NSTL
Elsevier
