University of Hertfordshire

By the same authors

Estimation of microphysical parameters of atmospheric pollution using machine learning

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

Documents

  • Article_v5

    Accepted author manuscript, 553 KB, PDF document

View graph of relations
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 Verlag
Pages579-588
Number of pages10
ISBN (Electronic)9783030014186
ISBN (Print)9783030014179
DOIs
Publication statusPublished - 27 Sep 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
CountryGreece
CityRhodes
Period4/10/187/10/18

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.

Notes

© 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.

ID: 18158733