Estimation of microphysical parameters of atmospheric pollution using machine learning

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

doi:10.1007/978-3-030-01418-6_57

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 proceeding › Conference contribution

1 Citation (Scopus)

23 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 language	English
Title of host publication	Artificial Neural Networks and Machine Learning – ICANN 2018
Subtitle of host publication	27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part I
Editors	Vera Kurkova, Barbara Hammer, Yannis Manolopoulos, Lazaros Iliadis, Ilias Maglogiannis
Publisher	Springer Nature Link
Pages	579-588
Number of pages	10
ISBN (Electronic)	9783030014186
ISBN (Print)	9783030014179
DOIs	https://doi.org/10.1007/978-3-030-01418-6_57
Publication status	Published - 27 Sept 2018
Event	27th International Conference on Artificial Neural Networks, ICANN 2018 - Rhodes, Greece Duration: 4 Oct 2018 → 7 Oct 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11139 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	27th International Conference on Artificial Neural Networks, ICANN 2018
Country/Territory	Greece
City	Rhodes
Period	4/10/18 → 7/10/18

Keywords

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

Access to Document

10.1007/978-3-030-01418-6_57Licence: Unspecified

Article_v5
© 2018 Springer-Verlag. This is a post-peer-review, pre-copyedit version of a paper published in Artificial Neural Networks and Machine Learning – ICANN 2018. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-01418-6_57.
Accepted author manuscript, 553 KBLicence: Unspecified

Cite this

Llerena, C., Müller, D., Adams, R., Davey, N., & Sun, Y. (2018). Estimation of microphysical parameters of atmospheric pollution using machine learning. In V. Kurkova, B. Hammer, Y. Manolopoulos, L. Iliadis, & I. Maglogiannis (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part I (pp. 579-588). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11139 LNCS). Springer Nature Link. https://doi.org/10.1007/978-3-030-01418-6_57

Llerena, C. ; Müller, D. ; Adams, R. et al. / Estimation of microphysical parameters of atmospheric pollution using machine learning. Artificial Neural Networks and Machine Learning – ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part I. editor / Vera Kurkova ; Barbara Hammer ; Yannis Manolopoulos ; Lazaros Iliadis ; Ilias Maglogiannis. Springer Nature Link, 2018. pp. 579-588 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{5023b807426b410e96ab2188de40de79,

title = "Estimation of microphysical parameters of atmospheric pollution using machine learning",

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 = "Complex refractive index, Effective radius, K-Nearest neighbor, LIDAR, Particle backscatter, Particle extinction coefficient",

author = "C. Llerena and D. M{\"u}ller and R. Adams and N. Davey and Y. Sun",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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pages = "579--588",

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Llerena, C, Müller, D , Adams, R, Davey, N & Sun, Y 2018, Estimation of microphysical parameters of atmospheric pollution using machine learning. in V Kurkova, B Hammer, Y Manolopoulos, L Iliadis & I Maglogiannis (eds), Artificial Neural Networks and Machine Learning – ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part I. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11139 LNCS, Springer Nature Link, pp. 579-588, 27th International Conference on Artificial Neural Networks, ICANN 2018, Rhodes, Greece, 4/10/18. https://doi.org/10.1007/978-3-030-01418-6_57

Estimation of microphysical parameters of atmospheric pollution using machine learning. / Llerena, C.; Müller, D.; Adams, R. et al.
Artificial Neural Networks and Machine Learning – ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part I. ed. / Vera Kurkova; Barbara Hammer; Yannis Manolopoulos; Lazaros Iliadis; Ilias Maglogiannis. Springer Nature Link, 2018. p. 579-588 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11139 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Estimation of microphysical parameters of atmospheric pollution using machine learning

AU - Llerena, C.

AU - Müller, D.

AU - Adams, R.

AU - Davey, N.

AU - Sun, Y.

N1 - © 2018 Springer-Verlag. This is a post-peer-review, pre-copyedit version of a paper published in Artificial Neural Networks and Machine Learning – ICANN 2018. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-01418-6_57.

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AB - 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.

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KW - Effective radius

KW - K-Nearest neighbor

KW - LIDAR

KW - Particle backscatter

KW - Particle extinction coefficient

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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A2 - Hammer, Barbara

A2 - Manolopoulos, Yannis

A2 - Iliadis, Lazaros

A2 - Maglogiannis, Ilias

PB - Springer Nature Link

T2 - 27th International Conference on Artificial Neural Networks, ICANN 2018

Y2 - 4 October 2018 through 7 October 2018

ER -

Llerena C, Müller D , Adams R, Davey N, Sun Y. Estimation of microphysical parameters of atmospheric pollution using machine learning. In Kurkova V, Hammer B, Manolopoulos Y, Iliadis L, Maglogiannis I, editors, Artificial Neural Networks and Machine Learning – ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part I. Springer Nature Link. 2018. p. 579-588. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-01418-6_57

Estimation of microphysical parameters of atmospheric pollution using machine learning

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