首页|Findings from Nankai University Broaden Understanding of Machine Learning (Analy sing the Spatiotemporal Variation and Influencing Factors of Lake Chaohu's Cdom Over the Past 40 Years Using Machine Learning)
Findings from Nankai University Broaden Understanding of Machine Learning (Analy sing the Spatiotemporal Variation and Influencing Factors of Lake Chaohu's Cdom Over the Past 40 Years Using Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news reporting from Tianjin, People's Republic of C hina, by NewsRx journalists, research stated, "Chromophoric dissolved organic ma tter (CDOM) in aquatic environments is an important component of the biogeochemi cal cycle and carbon cycle. The aim of this study is to investigate the long-ter m changes in CDOM in shallow and eutrophic Chaohu Lake, as well as its relations hip with climate, environment and social factors." Financial support for this research came from National Key R&D Prog ram of China. The news correspondents obtained a quote from the research from Nankai Universit y, "Using long time series Landsat image data and machine learning technology, t he spatiotemporal evolution of Chaohu CDOM since 1987 was reconstructed. A total of 180 samples were collected, which were divided into three parts based on reg ional and hydrological characteristics. The results show that the water quality in different regions were significantly different, and TN may be the key factor driving the change of CDOM in Chaohu Lake. Machine learning algorithms including random forest (RF), support vector regression (SVR), neural network (NN), multi modal deep learning (MDL) model and Extreme Gradient Boosting (XGBoost) were use d, among which XGBoost model performed best (R-2 = 0.955, mean absolute error [MAE] = 0.024 mg/L, root mean square error [RMSE] = 0.036 mg/L, bias = 1.005) and was used for CDOM spati otemporal variation retrieval. The change of CDOM was seasonal, highest in Augus t (0.67 m(-1)) and lowest in December (0.48 m(-1)), and the western lake is the main source of CDOM. Annual variability of the CDOM indicates that it began to d ecline after the completion of water pollution control in 2000. Temperature chan ges were closely related to CDOM (P <0.01) and agricultura l non-point source pollution plays an important role in Chaohu Lake."
TianjinPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningNankai University
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