A Machine Learning Approach to Predict Air Quality in California

Detalhes bibliográficos
Autor(a) principal: Castelli, Mauro
Data de Publicação: 2020
Outros Autores: Clemente, Fabiana Martins, Popovič, Aleš, Silva, Sara, Vanneschi, Leonardo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/103211
Resumo: Castelli, M., Clemente, F. M., Popovič, A., Silva, S., & Vanneschi, L. (2020). A Machine Learning Approach to Predict Air Quality in California. Complexity, 2020, 1-23. [8049504]. https://doi.org/10.1155/2020/8049504
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spelling A Machine Learning Approach to Predict Air Quality in CaliforniaComputer Science(all)GeneralSDG 11 - Sustainable Cities and CommunitiesCastelli, M., Clemente, F. M., Popovič, A., Silva, S., & Vanneschi, L. (2020). A Machine Learning Approach to Predict Air Quality in California. Complexity, 2020, 1-23. [8049504]. https://doi.org/10.1155/2020/8049504Predicting air quality is a complex task due to the dynamic nature, volatility, and high variability in time and space of pollutants and particulates. At the same time, being able to model, predict, and monitor air quality is becoming more and more relevant, especially in urban areas, due to the observed critical impact of air pollution on citizens' health and the environment. In this paper, we employ a popular machine learning method, support vector regression (SVR), to forecast pollutant and particulate levels and to predict the air quality index (AQI). Among the various tested alternatives, radial basis function (RBF) was the type of kernel that allowed SVR to obtain the most accurate predictions. Using the whole set of available variables revealed a more successful strategy than selecting features using principal component analysis. The presented results demonstrate that SVR with RBF kernel allows us to accurately predict hourly pollutant concentrations, like carbon monoxide, sulfur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, as well as the hourly AQI for the state of California. Classification into six AQI categories defined by the US Environmental Protection Agency was performed with an accuracy of 94.1% on unseen validation data.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNCastelli, MauroClemente, Fabiana MartinsPopovič, AlešSilva, SaraVanneschi, Leonardo2020-09-01T23:23:21Z2020-08-042020-08-04T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article23application/pdfhttp://hdl.handle.net/10362/103211eng1076-2787PURE: 19656506https://doi.org/10.1155/2020/8049504info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:48:39Zoai:run.unl.pt:10362/103211Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:39:49.415489Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A Machine Learning Approach to Predict Air Quality in California
title A Machine Learning Approach to Predict Air Quality in California
spellingShingle A Machine Learning Approach to Predict Air Quality in California
Castelli, Mauro
Computer Science(all)
General
SDG 11 - Sustainable Cities and Communities
title_short A Machine Learning Approach to Predict Air Quality in California
title_full A Machine Learning Approach to Predict Air Quality in California
title_fullStr A Machine Learning Approach to Predict Air Quality in California
title_full_unstemmed A Machine Learning Approach to Predict Air Quality in California
title_sort A Machine Learning Approach to Predict Air Quality in California
author Castelli, Mauro
author_facet Castelli, Mauro
Clemente, Fabiana Martins
Popovič, Aleš
Silva, Sara
Vanneschi, Leonardo
author_role author
author2 Clemente, Fabiana Martins
Popovič, Aleš
Silva, Sara
Vanneschi, Leonardo
author2_role author
author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Castelli, Mauro
Clemente, Fabiana Martins
Popovič, Aleš
Silva, Sara
Vanneschi, Leonardo
dc.subject.por.fl_str_mv Computer Science(all)
General
SDG 11 - Sustainable Cities and Communities
topic Computer Science(all)
General
SDG 11 - Sustainable Cities and Communities
description Castelli, M., Clemente, F. M., Popovič, A., Silva, S., & Vanneschi, L. (2020). A Machine Learning Approach to Predict Air Quality in California. Complexity, 2020, 1-23. [8049504]. https://doi.org/10.1155/2020/8049504
publishDate 2020
dc.date.none.fl_str_mv 2020-09-01T23:23:21Z
2020-08-04
2020-08-04T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/103211
url http://hdl.handle.net/10362/103211
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1076-2787
PURE: 19656506
https://doi.org/10.1155/2020/8049504
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 23
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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