Multilayer perceptron and regression modelling to forecast hourly nitrogen dioxide concentrations

This paper presents the application of feed-forward multilayer perceptron networks and multiple regression models, to forecast hourly nitrogen dioxide levels 24 hours in advance. Input data are traffic and meteorological variables, and nitrogen dioxide hourly levels. The introduction of four periodi...

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Detalles Bibliográficos
Autor: Capilla, Carmen
Tipo de recurso: capítulo de libro
Fecha de publicación:2014
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/52221
Acceso en línea:https://riunet.upv.es/handle/10251/52221
Access Level:acceso abierto
Palabra clave:Air quality
Nitrogen dioxide concentration
Urban atmosphere pollution
Multilayer perceptron
Multiple regression model
ESTADISTICA E INVESTIGACION OPERATIVA
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spelling Multilayer perceptron and regression modelling to forecast hourly nitrogen dioxide concentrationsCapilla, CarmenAir qualityNitrogen dioxide concentrationUrban atmosphere pollutionMultilayer perceptronMultiple regression modelESTADISTICA E INVESTIGACION OPERATIVAThis paper presents the application of feed-forward multilayer perceptron networks and multiple regression models, to forecast hourly nitrogen dioxide levels 24 hours in advance. Input data are traffic and meteorological variables, and nitrogen dioxide hourly levels. The introduction of four periodic components (sine and cosine terms for the daily and weekly cycles), and nitrogen oxide hourly levels was analyzed in order to improve the prediction power. The data were measured for three years at two monitoring stations in Valencia (Spain). The model evaluation criteria were the mean absolute error, the root mean square error and the mean absolute percentage error. The multilayer perceptron networks performed better than the regression models in nonlinear relationships like that involving nitrogen oxides, meteorological and traffic variables. Comparisons of the multilayer perceptron-based models proved that the insertion of the four additional seasonal input variables improved the ability of obtaining more accurate predictions, which emphasizes the importance of taking into account the seasonal character of nitrogen dioxide. The advantages of neural networks were that they did not require very exhaustive information about air pollutants, reaction mechanisms, meteorological parameters or traffic flow, and that they had the ability of allowing nonlinear relationships between very different predictor variables in an urban environment.WIT PressRepositorio Institucional de la Universitat Politècnica de València Riunet20142014-01-01book parthttp://purl.org/coar/resource_type/c_3248VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/bookPartapplication/pdfhttps://riunet.upv.es/handle/10251/52221reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reserva de todos los derechoshttp://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/522212026-06-13T07:49:27Z
dc.title.none.fl_str_mv Multilayer perceptron and regression modelling to forecast hourly nitrogen dioxide concentrations
title Multilayer perceptron and regression modelling to forecast hourly nitrogen dioxide concentrations
spellingShingle Multilayer perceptron and regression modelling to forecast hourly nitrogen dioxide concentrations
Capilla, Carmen
Air quality
Nitrogen dioxide concentration
Urban atmosphere pollution
Multilayer perceptron
Multiple regression model
ESTADISTICA E INVESTIGACION OPERATIVA
title_short Multilayer perceptron and regression modelling to forecast hourly nitrogen dioxide concentrations
title_full Multilayer perceptron and regression modelling to forecast hourly nitrogen dioxide concentrations
title_fullStr Multilayer perceptron and regression modelling to forecast hourly nitrogen dioxide concentrations
title_full_unstemmed Multilayer perceptron and regression modelling to forecast hourly nitrogen dioxide concentrations
title_sort Multilayer perceptron and regression modelling to forecast hourly nitrogen dioxide concentrations
dc.creator.none.fl_str_mv Capilla, Carmen
author Capilla, Carmen
author_facet Capilla, Carmen
author_role author
dc.contributor.none.fl_str_mv Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Air quality
Nitrogen dioxide concentration
Urban atmosphere pollution
Multilayer perceptron
Multiple regression model
ESTADISTICA E INVESTIGACION OPERATIVA
topic Air quality
Nitrogen dioxide concentration
Urban atmosphere pollution
Multilayer perceptron
Multiple regression model
ESTADISTICA E INVESTIGACION OPERATIVA
description This paper presents the application of feed-forward multilayer perceptron networks and multiple regression models, to forecast hourly nitrogen dioxide levels 24 hours in advance. Input data are traffic and meteorological variables, and nitrogen dioxide hourly levels. The introduction of four periodic components (sine and cosine terms for the daily and weekly cycles), and nitrogen oxide hourly levels was analyzed in order to improve the prediction power. The data were measured for three years at two monitoring stations in Valencia (Spain). The model evaluation criteria were the mean absolute error, the root mean square error and the mean absolute percentage error. The multilayer perceptron networks performed better than the regression models in nonlinear relationships like that involving nitrogen oxides, meteorological and traffic variables. Comparisons of the multilayer perceptron-based models proved that the insertion of the four additional seasonal input variables improved the ability of obtaining more accurate predictions, which emphasizes the importance of taking into account the seasonal character of nitrogen dioxide. The advantages of neural networks were that they did not require very exhaustive information about air pollutants, reaction mechanisms, meteorological parameters or traffic flow, and that they had the ability of allowing nonlinear relationships between very different predictor variables in an urban environment.
publishDate 2014
dc.date.none.fl_str_mv 2014
2014-01-01
dc.type.none.fl_str_mv book part
http://purl.org/coar/resource_type/c_3248
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/52221
url https://riunet.upv.es/handle/10251/52221
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reserva de todos los derechos
http://rightsstatements.org/vocab/InC/1.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv WIT Press
publisher.none.fl_str_mv WIT Press
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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