首页|Study Data from Russian Academy of Sciences Provide New Insights into Machine Learning (Smap Sea Surface Salinity Improvement In the Arctic Region Using Machine Learning Approaches)
Study Data from Russian Academy of Sciences Provide New Insights into Machine Learning (Smap Sea Surface Salinity Improvement In the Arctic Region Using Machine Learning Approaches)
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Investigators discuss new findings in Machine Learning. According to news originating from Moscow, Russia, by NewsRx correspondents, research stated, “Sea surface salinity (SSS) is a key physicochemical characteristic of the ocean that plays a significant role in describing the climate. Routine SSS retrieval algorithms exploiting remote sensing data have been developed and validated with high precision for typical regions of the World Ocean.” Funders for this research include Moscow Institute of Physics and Technology Development Program (Priority-2030), Russian Science Foundation (RSF). Our news journalists obtained a quote from the research from the Russian Academy of Sciences, “Their effectiveness is worse in the Arctic though. To address this limitation, in this study, we employ machine learning (ML) techniques to enhance the quality of standard algorithms. We evaluate a few ML models, ranging from classical methods that process vector features, provided by standard Soil Moisture Active Passive (SMAP) satellite salinity algorithms, to deep artificial neural networks that combine vector features with two-dimensional fields extracted from the ERA5 reanalysis. We validate these models using in situ the data collected by the Shirshov Institute of Oceanology RAS during the expeditions to the Barents, Kara, Laptev, and East Siberian seas from 2015 to 2021. The results of the study indicate that the SMAP sea surface salinity standard product is improved in these regions.”
MoscowRussiaAlgorithmsCyborgsEmerging TechnologiesMachine LearningRussian Academy of Sciences
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Pleiades Publishing Inc
