首页|Research Reports from Shanghai Normal University Provide New Insights into Machi ne Learning (Mapping seamless monthly XCO2 in East Asia: Utilizing OCO-2 data an d machine learning)
Research Reports from Shanghai Normal University Provide New Insights into Machi ne Learning (Mapping seamless monthly XCO2 in East Asia: Utilizing OCO-2 data an d machine learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news reporting originating fr om Shanghai, People’s Republic of China, by NewsRx correspondents, research stat ed, “High spatial resolution XCO2 data is key to investigating the mechanisms of carbon sources and sinks.” The news correspondents obtained a quote from the research from Shanghai Normal University: “However, current carbon satellites have a narrow swath and uneven o bservation points, making it difficult to obtain seamless and full-coverage data . We propose a novel method combining extreme gradient boosting (XGBoost) with p article swarm optimization (PSO) to construct the relationship between OCO-2 XCO 2 data and auxiliary data (i.e., vegetation, meteorological, anthropogenic emiss ions, and LST data), and to map the seamless monthly XCO2 concentration in East Asia from 2015 to 2020. Validation results based on TCCON ground station data de monstrate the high accuracy of the model with an average R2 of 0.93, Root Mean S quare Error (RMSE) of 1.33 and Mean Absolute Percentage Error (MAPE) of 0.24 % in five sites. The results show that the average atmospheric XCO2 concentration in East Asia shows a continuous increasing trend from 2015 to 2020, with an aver age annual growth rate of 2.21 ppm/yr. This trend is accompanied by clear season al variations, with the highest XCO2 concentration in winter and the lowest in s ummer. Additionally, anthropogenic activities contributed significantly to XCO2 concentrations, which were higher in urban areas.”
Shanghai Normal UniversityShanghaiPe ople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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