首页|Reports Outline Machine Learning Study Results from Sun Yat-sen University (Robu st Remote Sensing Retrieval of Key Eutrophication Indicators In Coastal Waters B ased On Explainable Machine Learning)
Reports Outline Machine Learning Study Results from Sun Yat-sen University (Robu st Remote Sensing Retrieval of Key Eutrophication Indicators In Coastal Waters B ased On Explainable Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-A new study on Machine Learning is now available. According to news reporting from Zhuhai, People's Republic of China, by NewsRx journalists, research stated, "Excessive discharges of nitrogen and phosphorus n utrients lead to eutrophication in coastal waters. Optical remote sensing retrie val of the key eutrophication indicators, namely dissolved inorganic nitrogen co ncentration (DIN), soluble reactive phosphate concentration (SRP), and chemical oxygen demand (COD), remains challenging due to lack of distinct spectral featur es." Financial supporters for this research include Southern Marine Science and Engin eering Guangdong Laboratory (Zhuhai), Fundamental Research Funds for the Central Universities, China-Korea Joint Ocean Research Center, China.
ZhuhaiPeople's Republic of ChinaAsiaAlgorithmsCyborgsEmerging TechnologiesMachine LearningRemote SensingSun Yat-sen University
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