dc.contributor.author | Llerena, C. | |
dc.contributor.author | Müller, D. | |
dc.contributor.author | Adams, R. | |
dc.contributor.author | Davey, N. | |
dc.contributor.author | Sun, Y. | |
dc.contributor.editor | Kurkova, Vera | |
dc.contributor.editor | Hammer, Barbara | |
dc.contributor.editor | Manolopoulos, Yannis | |
dc.contributor.editor | Iliadis, Lazaros | |
dc.contributor.editor | Maglogiannis, Ilias | |
dc.date.accessioned | 2020-04-01T00:02:57Z | |
dc.date.available | 2020-04-01T00:02:57Z | |
dc.date.issued | 2018-09-27 | |
dc.identifier.citation | 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 , 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 | |
dc.identifier.citation | conference | |
dc.identifier.isbn | 9783030014179 | |
dc.identifier.isbn | 9783030014186 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.other | ORCID: /0000-0002-0203-7654/work/71186196 | |
dc.identifier.uri | http://hdl.handle.net/2299/22534 | |
dc.description | © 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. | |
dc.description.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. | en |
dc.format.extent | 10 | |
dc.format.extent | 566107 | |
dc.language.iso | eng | |
dc.publisher | Springer Nature | |
dc.relation.ispartof | Artificial Neural Networks and Machine Learning – ICANN 2018 | |
dc.relation.ispartofseries | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
dc.subject | Complex refractive index | |
dc.subject | Effective radius | |
dc.subject | K-Nearest neighbor | |
dc.subject | LIDAR | |
dc.subject | Particle backscatter | |
dc.subject | Particle extinction coefficient | |
dc.subject | Theoretical Computer Science | |
dc.subject | General Computer Science | |
dc.title | Estimation of microphysical parameters of atmospheric pollution using machine learning | en |
dc.contributor.institution | School of Physics, Astronomy and Mathematics | |
dc.contributor.institution | Centre for Computer Science and Informatics Research | |
dc.contributor.institution | SPECS Deans Group | |
dc.contributor.institution | Centre for Atmospheric and Climate Physics Research | |
dc.contributor.institution | School of Computer Science | |
dc.contributor.institution | Biocomputation Research Group | |
dc.date.embargoedUntil | 2019-09-27 | |
dc.identifier.url | http://www.scopus.com/inward/record.url?scp=85054852779&partnerID=8YFLogxK | |
rioxxterms.versionofrecord | 10.1007/978-3-030-01418-6_57 | |
rioxxterms.type | Other | |
herts.preservation.rarelyaccessed | true | |