Application of a Fuzzy ARTMAP Neural Network for Indoor Air Quality Prediction

Detalhes bibliográficos
Autor(a) principal: Ferreira, Willian De Assis Pedrobon [UNESP]
Data de Publicação: 2022
Outros Autores: Grout, Ian, Silva, Alexandre Cesar Rodrigues da[UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/iEECON53204.2022.9741563
http://hdl.handle.net/11449/239874
Resumo: Indoor air quality monitoring is an important activity to ensure continued health and well-being of citizens living, studying, and working in indoor environments. This practice has been widely developed through the application of low-cost sensors that are able to measure gas concentrations, particulate matter, and other components such as humidity and temperature that affect indoor air quality. Additionally, machine learning algorithms have been applied in the interpretation of sampled environmental data to improve the performance of monitoring systems. This paper proposes the implementation of a fuzzy ARTMAP neural network, which employs the concepts of Adaptive Resonance Theory (ART), to compute the prediction of particulate matter sampled in a domestic bedroom environment. With the application of a specialized online training architecture, the fuzzy ARTMAP network can be a promising alternative to predict particulate matter time series data modeled in sliding windows, obtaining predictions 24-hour ahead with mean absolute error (MAE) ranging here from 0.26 to 7.65.
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spelling Application of a Fuzzy ARTMAP Neural Network for Indoor Air Quality Predictionfuzzy ARTMAP neural networkindoor air qualityonline trainingparticulate matter predictionIndoor air quality monitoring is an important activity to ensure continued health and well-being of citizens living, studying, and working in indoor environments. This practice has been widely developed through the application of low-cost sensors that are able to measure gas concentrations, particulate matter, and other components such as humidity and temperature that affect indoor air quality. Additionally, machine learning algorithms have been applied in the interpretation of sampled environmental data to improve the performance of monitoring systems. This paper proposes the implementation of a fuzzy ARTMAP neural network, which employs the concepts of Adaptive Resonance Theory (ART), to compute the prediction of particulate matter sampled in a domestic bedroom environment. With the application of a specialized online training architecture, the fuzzy ARTMAP network can be a promising alternative to predict particulate matter time series data modeled in sliding windows, obtaining predictions 24-hour ahead with mean absolute error (MAE) ranging here from 0.26 to 7.65.São Paulo State University Department of Electrical EngineeringUniversity of Limerick Department of Electronic and Computer EngineeringSão Paulo State University Department of Electrical EngineeringUniversidade Estadual Paulista (UNESP)University of LimerickFerreira, Willian De Assis Pedrobon [UNESP]Grout, IanSilva, Alexandre Cesar Rodrigues da[UNESP]2023-03-01T19:51:21Z2023-03-01T19:51:21Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/iEECON53204.2022.9741563Proceedings of the 2022 International Electrical Engineering Congress, iEECON 2022.http://hdl.handle.net/11449/23987410.1109/iEECON53204.2022.97415632-s2.0-85128177831Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings of the 2022 International Electrical Engineering Congress, iEECON 2022info:eu-repo/semantics/openAccess2023-03-01T19:51:21Zoai:repositorio.unesp.br:11449/239874Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:55:08.614013Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Application of a Fuzzy ARTMAP Neural Network for Indoor Air Quality Prediction
title Application of a Fuzzy ARTMAP Neural Network for Indoor Air Quality Prediction
spellingShingle Application of a Fuzzy ARTMAP Neural Network for Indoor Air Quality Prediction
Ferreira, Willian De Assis Pedrobon [UNESP]
fuzzy ARTMAP neural network
indoor air quality
online training
particulate matter prediction
title_short Application of a Fuzzy ARTMAP Neural Network for Indoor Air Quality Prediction
title_full Application of a Fuzzy ARTMAP Neural Network for Indoor Air Quality Prediction
title_fullStr Application of a Fuzzy ARTMAP Neural Network for Indoor Air Quality Prediction
title_full_unstemmed Application of a Fuzzy ARTMAP Neural Network for Indoor Air Quality Prediction
title_sort Application of a Fuzzy ARTMAP Neural Network for Indoor Air Quality Prediction
author Ferreira, Willian De Assis Pedrobon [UNESP]
author_facet Ferreira, Willian De Assis Pedrobon [UNESP]
Grout, Ian
Silva, Alexandre Cesar Rodrigues da[UNESP]
author_role author
author2 Grout, Ian
Silva, Alexandre Cesar Rodrigues da[UNESP]
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
University of Limerick
dc.contributor.author.fl_str_mv Ferreira, Willian De Assis Pedrobon [UNESP]
Grout, Ian
Silva, Alexandre Cesar Rodrigues da[UNESP]
dc.subject.por.fl_str_mv fuzzy ARTMAP neural network
indoor air quality
online training
particulate matter prediction
topic fuzzy ARTMAP neural network
indoor air quality
online training
particulate matter prediction
description Indoor air quality monitoring is an important activity to ensure continued health and well-being of citizens living, studying, and working in indoor environments. This practice has been widely developed through the application of low-cost sensors that are able to measure gas concentrations, particulate matter, and other components such as humidity and temperature that affect indoor air quality. Additionally, machine learning algorithms have been applied in the interpretation of sampled environmental data to improve the performance of monitoring systems. This paper proposes the implementation of a fuzzy ARTMAP neural network, which employs the concepts of Adaptive Resonance Theory (ART), to compute the prediction of particulate matter sampled in a domestic bedroom environment. With the application of a specialized online training architecture, the fuzzy ARTMAP network can be a promising alternative to predict particulate matter time series data modeled in sliding windows, obtaining predictions 24-hour ahead with mean absolute error (MAE) ranging here from 0.26 to 7.65.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-01
2023-03-01T19:51:21Z
2023-03-01T19:51:21Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/iEECON53204.2022.9741563
Proceedings of the 2022 International Electrical Engineering Congress, iEECON 2022.
http://hdl.handle.net/11449/239874
10.1109/iEECON53204.2022.9741563
2-s2.0-85128177831
url http://dx.doi.org/10.1109/iEECON53204.2022.9741563
http://hdl.handle.net/11449/239874
identifier_str_mv Proceedings of the 2022 International Electrical Engineering Congress, iEECON 2022.
10.1109/iEECON53204.2022.9741563
2-s2.0-85128177831
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Proceedings of the 2022 International Electrical Engineering Congress, iEECON 2022
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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