Prediction of PM10 concentrations for Bahía Blanca, Argentina

Autores
Brignole, Nélida B.; Chiarvetto Peralta, Lucila L.; Díaz, Mónica F.
Año de publicación
2012
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
PM10 non-traditional modelling for e-government development is described in detail. Ambient PM10 concentrations were predicted using meteorological variables as inputs, whose relevance for a generated Artificial Neural Network was analyzed by a feature selection method. The work is specially focused on the surroundings of Bahía Blanca city, its petrochemical pole and Ing. White grain port. Its accuracy was tested with time windows ranging from 2004 to 2006. A trustworthy simulation of the physical phenomena was built. As a result, this predictive model will contribute to the local observatory in order to trigger early-alert warnings.
Eje: Workshop Agentes y sistemas inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Intelligent agents
Neural nets
artificial neural network
particulate matter
PM10
forecast model
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/23592
Acceder
id SEDICI_3f9ebdac3b523280b2bbded0879abe6a
oai_identifier_str oai:sedici.unlp.edu.ar:10915/23592
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Prediction of PM10 concentrations for Bahía Blanca, ArgentinaBrignole, Nélida B.Chiarvetto Peralta, Lucila L.Díaz, Mónica F.Ciencias InformáticasIntelligent agentsNeural netsartificial neural networkparticulate matterPM10forecast modelPM10 non-traditional modelling for e-government development is described in detail. Ambient PM10 concentrations were predicted using meteorological variables as inputs, whose relevance for a generated Artificial Neural Network was analyzed by a feature selection method. The work is specially focused on the surroundings of Bahía Blanca city, its petrochemical pole and Ing. White grain port. Its accuracy was tested with time windows ranging from 2004 to 2006. A trustworthy simulation of the physical phenomena was built. As a result, this predictive model will contribute to the local observatory in order to trigger early-alert warnings.Eje: Workshop Agentes y sistemas inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI)2012-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/23592enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T16:37:04Zoai:sedici.unlp.edu.ar:10915/23592Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 16:37:04.757SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Prediction of PM10 concentrations for Bahía Blanca, Argentina
title Prediction of PM10 concentrations for Bahía Blanca, Argentina
spellingShingle Prediction of PM10 concentrations for Bahía Blanca, Argentina
Brignole, Nélida B.
Ciencias Informáticas
Intelligent agents
Neural nets
artificial neural network
particulate matter
PM10
forecast model
title_short Prediction of PM10 concentrations for Bahía Blanca, Argentina
title_full Prediction of PM10 concentrations for Bahía Blanca, Argentina
title_fullStr Prediction of PM10 concentrations for Bahía Blanca, Argentina
title_full_unstemmed Prediction of PM10 concentrations for Bahía Blanca, Argentina
title_sort Prediction of PM10 concentrations for Bahía Blanca, Argentina
dc.creator.none.fl_str_mv Brignole, Nélida B.
Chiarvetto Peralta, Lucila L.
Díaz, Mónica F.
author Brignole, Nélida B.
author_facet Brignole, Nélida B.
Chiarvetto Peralta, Lucila L.
Díaz, Mónica F.
author_role author
author2 Chiarvetto Peralta, Lucila L.
Díaz, Mónica F.
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Intelligent agents
Neural nets
artificial neural network
particulate matter
PM10
forecast model
topic Ciencias Informáticas
Intelligent agents
Neural nets
artificial neural network
particulate matter
PM10
forecast model
dc.description.none.fl_txt_mv PM10 non-traditional modelling for e-government development is described in detail. Ambient PM10 concentrations were predicted using meteorological variables as inputs, whose relevance for a generated Artificial Neural Network was analyzed by a feature selection method. The work is specially focused on the surroundings of Bahía Blanca city, its petrochemical pole and Ing. White grain port. Its accuracy was tested with time windows ranging from 2004 to 2006. A trustworthy simulation of the physical phenomena was built. As a result, this predictive model will contribute to the local observatory in order to trigger early-alert warnings.
Eje: Workshop Agentes y sistemas inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
description PM10 non-traditional modelling for e-government development is described in detail. Ambient PM10 concentrations were predicted using meteorological variables as inputs, whose relevance for a generated Artificial Neural Network was analyzed by a feature selection method. The work is specially focused on the surroundings of Bahía Blanca city, its petrochemical pole and Ing. White grain port. Its accuracy was tested with time windows ranging from 2004 to 2006. A trustworthy simulation of the physical phenomena was built. As a result, this predictive model will contribute to the local observatory in order to trigger early-alert warnings.
publishDate 2012
dc.date.none.fl_str_mv 2012-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/23592
url http://sedici.unlp.edu.ar/handle/10915/23592
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
_version_ 1846782830724513792
score 12.982451

Publicaciones similares