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Estimation of microphysical parameters of atmospheric pollution using machine learning

dc.contributor.authorLlerena, C.
dc.contributor.authorMüller, D.
dc.contributor.authorAdams, R.
dc.contributor.authorDavey, N.
dc.contributor.authorSun, Y.
dc.contributor.editorKurkova, Vera
dc.contributor.editorHammer, Barbara
dc.contributor.editorManolopoulos, Yannis
dc.contributor.editorIliadis, Lazaros
dc.contributor.editorMaglogiannis, Ilias
dc.date.accessioned2020-04-01T00:02:57Z
dc.date.available2020-04-01T00:02:57Z
dc.date.issued2018-09-27
dc.identifier.citationLlerena , 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.citationconference
dc.identifier.isbn9783030014179
dc.identifier.isbn9783030014186
dc.identifier.issn0302-9743
dc.identifier.otherORCID: /0000-0002-0203-7654/work/71186196
dc.identifier.urihttp://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.abstractThe 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.extent10
dc.format.extent566107
dc.language.isoeng
dc.publisherSpringer Nature
dc.relation.ispartofArtificial Neural Networks and Machine Learning – ICANN 2018
dc.relation.ispartofseriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.subjectComplex refractive index
dc.subjectEffective radius
dc.subjectK-Nearest neighbor
dc.subjectLIDAR
dc.subjectParticle backscatter
dc.subjectParticle extinction coefficient
dc.subjectTheoretical Computer Science
dc.subjectGeneral Computer Science
dc.titleEstimation of microphysical parameters of atmospheric pollution using machine learningen
dc.contributor.institutionSchool of Physics, Astronomy and Mathematics
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionSPECS Deans Group
dc.contributor.institutionCentre for Atmospheric and Climate Physics Research
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionBiocomputation Research Group
dc.date.embargoedUntil2019-09-27
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85054852779&partnerID=8YFLogxK
rioxxterms.versionofrecord10.1007/978-3-030-01418-6_57
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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