Classifying aerosols with machine learning techniques using the AERONET and CALIPSO satellite databases

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dc.contributor.authorCACHEFFO, A.pt_BR
dc.contributor.authorLOPES, F.J.S.pt_BR
dc.contributor.authorYOSHIDA, A.C.pt_BR
dc.contributor.authorLANDULFO, E.pt_BR
dc.coverageInternacionalpt_BR
dc.creator.eventoSP SCHOOL OF ADVANCED SCIENCE ON ATMOSPHERIC AEROSOLSpt_BR
dc.date.accessioned2021-09-06T12:10:07Z
dc.date.available2021-09-06T12:10:07Z
dc.date.eventoJuly 22 - August 2, 2019pt_BR
dc.description.abstractIn this work, our intention is to develop ways to correlate and classify several types of aerosols, by practical and objective manners, with the aim of machine learning techniques (specially decision trees and random forests) [1, 2]. For this purpose, we are intended to use the AERONET (Aerosol Robotic Network) and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite databases [3]. The AERONET database, which includes measurements made since year 2000, will provide to us a reference standard for the categorization and classification of aerosols present in atmosphere [3]. Following this, the databases for the measurements made by the CALIPSO satellite will be addressed, also with the objective of categorizing and classifying aerosols. Such data mining processes will enable us to carry out statistical and climatological analyzes of these databases, allowing a better study of the atmospheric behavior of aerosols in the Earth’s atmosphere [4]. We believe that the development of such tools and techniques for treatment of data provided by AERONET and CALIPSO will contribute greatly to a better understanding of climate change processes on Earth, a subject of scientific interest, especially in recent years.pt_BR
dc.event.siglaSPSASpt_BR
dc.format.extent102-102pt_BR
dc.identifier.citationCACHEFFO, A.; LOPES, F.J.S.; YOSHIDA, A.C.; LANDULFO, E. Classifying aerosols with machine learning techniques using the AERONET and CALIPSO satellite databases. In: SP SCHOOL OF ADVANCED SCIENCE ON ATMOSPHERIC AEROSOLS, July 22 - August 2, 2019, São Paulo, SP. <b>Abstract...</b> São Paulo, SP: Instituto de Física - USP, 2019. p. 102-102. Disponível em: http://repositorio.ipen.br/handle/123456789/32219.
dc.identifier.orcid0000-0002-9691-5306pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-9691-5306
dc.identifier.urihttp://repositorio.ipen.br/handle/123456789/32219
dc.localSão Paulo, SPpt_BR
dc.local.eventoSão Paulo, SPpt_BR
dc.publisherInstituto de Física - USPpt_BR
dc.rightsopenAccesspt_BR
dc.titleClassifying aerosols with machine learning techniques using the AERONET and CALIPSO satellite databasespt_BR
dc.typeResumo de eventos científicospt_BR
dspace.entity.typePublication
ipen.autorEDUARDO LANDULFO
ipen.autorFABIO JULIANO DA SILVA LOPES
ipen.codigoautor503
ipen.codigoautor6576
ipen.contributor.ipenauthorEDUARDO LANDULFO
ipen.contributor.ipenauthorFABIO JULIANO DA SILVA LOPES
ipen.date.recebimento21-09
ipen.event.datapadronizada2019pt_BR
ipen.identifier.ipendoc27988pt_BR
ipen.notas.internasAbstractpt_BR
ipen.type.genreResumo
relation.isAuthorOfPublicatione4dff370-e8c1-4437-846a-ef18a3ad606b
relation.isAuthorOfPublicationdbeb371a-361e-499e-a0ab-4826638fb1ca
relation.isAuthorOfPublication.latestForDiscoverydbeb371a-361e-499e-a0ab-4826638fb1ca
sigepi.autor.atividadeLANDULFO, E.:503:920:Npt_BR
sigepi.autor.atividadeLOPES, F.J.S.:6576:920:Npt_BR
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