Estimation of microphysical parameters of atmospheric pollution using machine learning
Llerena, C., Müller, D., Adams, R., Davey, N. and Sun, Y.
(2018)
Estimation of microphysical parameters of atmospheric pollution using machine learning.
Springer Nature.
The estimation of microphysical parameters of pollution (effective radius and complex refractive index) from optical aerosol parameters entails a complex problem. In previous work based on machine learning techniques, Artificial Neural Networks have been used to solve this problem. In this paper, the use of a classification and regression solution based on the k-Nearest Neighbor algorithm is proposed. Results show that this contribution achieves better results in terms of accuracy than the previous work.
Item Type | Other |
---|---|
Uncontrolled Keywords | Complex refractive index; Effective radius; K-Nearest neighbor; LIDAR; Particle backscatter; Particle extinction coefficient |
Subjects |
Mathematics(all) > Theoretical Computer Science Computer Science(all) |
Date Deposited | 27 Jul 2024 00:26 |
Last Modified | 27 Jul 2024 00:26 |
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- Centre for Atmospheric and Climate Physics Research
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