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Estimation of Microphysical Parameters of Atmospheric Pollution Using Machine Learning

  • C. Llerena
  • D. Müller
  • R. Adams
  • N. Davey
  • Y. Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11139)

Abstract

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.

Keywords

LIDAR Particle extinction coefficient Particle backscatter Effective radius Complex refractive index K-Nearest Neighbor 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • C. Llerena
    • 1
  • D. Müller
    • 2
  • R. Adams
    • 3
  • N. Davey
    • 1
    • 2
    • 3
  • Y. Sun
    • 1
    • 2
    • 3
  1. 1.Polytechnic School, University of AlcaláAlcalá de HenaresSpain
  2. 2.School of Physics, Astronomy and MathematicsUniversity of HertfordshireHertfordshireUK
  3. 3.Centre for Computer Science and Informatics Research, University of HertfordshireHertfordshireUK

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