首页|Reports from Chinese Academy of Sciences Advance Knowledge in Machine Learning ( Regional Divergent Evolution of Vegetation Greenness and Climatic Drivers In the Sahel-sudan-guinea Region: Nonlinearity and Explainable Machine Learning)
Reports from Chinese Academy of Sciences Advance Knowledge in Machine Learning ( Regional Divergent Evolution of Vegetation Greenness and Climatic Drivers In the Sahel-sudan-guinea Region: Nonlinearity and Explainable Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting originating in Beijing, Peo ple’s Republic of China, by NewsRx journalists, research stated, “The vegetation dynamics of the Sahel-Sudan-Guinea region in Africa, one of the largest transit ion zones between arid and humid zones, is of great significance for understandi ng regional ecosystem changes. However, a time-unvarying trend based on linear a ssumption challenges the overall understanding of vegetation greenness evolution and of tracking a complex ecosystem response to climate in the Sahel-Sudan-Guin ea region.Methods This study first applied the ensemble empirical mode decomposi tion (EEMD) method to detect the time-varying trends in vegetation greenness bas ed on normalized difference vegetation index (NDVI) data in the region during 20 01-2020, and then identified the dominant climatic drivers of NDVI trends by emp loying explainable machine learning framework.Results The study revealed an over all vegetation greening but a significant nonlinear spatio-temporal evolution ch aracteristic over the region.”
BeijingPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningChinese Academy of Sciences
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