Estimation of microphysical parameters of atmospheric pollution using machine learning

C. Llerena, D. Müller, R. Adams, N. Davey, Y. Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)
13 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2018
Subtitle of host publication27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part I
EditorsVera Kurkova, Barbara Hammer, Yannis Manolopoulos, Lazaros Iliadis, Ilias Maglogiannis
PublisherSpringer Nature
Pages579-588
Number of pages10
ISBN (Electronic)9783030014186
ISBN (Print)9783030014179
DOIs
Publication statusPublished - 27 Sept 2018
Event27th International Conference on Artificial Neural Networks, ICANN 2018 - Rhodes, Greece
Duration: 4 Oct 20187 Oct 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11139 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference27th International Conference on Artificial Neural Networks, ICANN 2018
Country/TerritoryGreece
CityRhodes
Period4/10/187/10/18

Keywords

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

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