首页|Study Data from Indian Institute of Astrophysics Update Understanding of Machine Learning (Aerosol Classification By Application of Machine Learning Spectral Cl ustering Algorithm)
Study Data from Indian Institute of Astrophysics Update Understanding of Machine Learning (Aerosol Classification By Application of Machine Learning Spectral Cl ustering Algorithm)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on Machine Learn ing. According to news reporting originating in Bangalore, India, by NewsRx jour nalists, research stated, "Precise understanding of aerosol classification is cr ucial for accurately quantifying the effects of aerosols on the Earth's energy b udget, improving remote sensing retrieval algorithms, formulating climate change related policies, and more. In this study, we used aerosol measurements from the quality assured AERosol Robotic NETwork (AERONET) and utilized a multivariate s pectral clustering algorithm, a machine learning tool, to classify global aeroso ls." Financial support for this research came from Ministry of Earth Science (MoES), Government of India. The news reporters obtained a quote from the research from the Indian Institute of Astrophysics, "The spectral clustering algorithm is a variant of the clusteri ng algorithm that employs eigenvalues and eigenvectors of the data matrix to pro ject the data into a lower -dimensional space of a similar cluster. To accomplis h this, we considered five aerosol optical parameters: fine -mode Aerosol Optica l Depth, Extinction Angstrom Exponent, Absorption Angstrom Exponent, Single Scat tering Albedo, and Refractive Index from 150 AERONET sites distributed in six co ntinents (Africa, Asia, Australia, Europe, North and South America) during 1993 to 2022. Using the clustering analysis, we identified four primary aerosol types : dust, urban, biomass burning, and mixed aerosols. Among the continents, the Af rican and Asian sites exhibited the highest contribution of dust aerosols, as th e region has significant global dust sources." According to the news reporters, the research concluded: "Conversely, Australia, Europe, North, and South America are predominantly influenced by fine -mode aer osols, given their considerable distance from major dust source regions."
BangaloreIndiaAsiaAlgorithmsCybo rgsEmerging TechnologiesMachine LearningIndian Institute of Astrophysics
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