Space-Time Prediction of PM2.5 Concentrations in Santiago de Chile Using LSTM Networks

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datacite.alternateIdentifier.citationApplied Sciences (Switzerland), 12 (22), 2022
datacite.alternateIdentifier.doi10.3390/app122211317
datacite.alternateIdentifier.issn2076-3417
datacite.creatorPeralta, Billy M.
datacite.creatorSepúlveda, Tomás
datacite.creatorNicolis, Orietta
datacite.creatorCaro, Luis Alberto
datacite.date2022
datacite.rightsAcceso abierto
datacite.subjectPm2.5
datacite.subjectPollution Model
datacite.subjectRecurrent Neural Networks
datacite.subjectSpace-time Prediction
datacite.titleSpace-Time Prediction of PM2.5 Concentrations in Santiago de Chile Using LSTM Networks
dc.date.accessioned2025-10-06T14:21:41Z
dc.date.available2025-10-06T14:21:41Z
dc.description.abstractCurrently, air pollution is a highly important issue in society due to its harmful effects on human health and the environment. The prediction of pollutant concentrations in Santiago de Chile is typically based on statistical methods or classical neural networks. Existing methods often assume that historical values are known at a fixed geographic point, such that air pollution can be predicted at a future hour using time series analysis. However, these methods are inapplicable when it is necessary to know the pollutant concentrations at every point of the space. This work proposes a method that addresses the space-time prediction of PM (Formula presented.) concentration in Santiago de Chile at any spatial points through the use of the LSTM recurrent network model. In particular, by considering historical values of air pollutants (PM (Formula presented.), PM (Formula presented.) and nitrogen dioxide) and meteorological variables (temperature, wind speed and direction and relative humidity), measured at fixed monitoring stations, the proposed model can predict PM (Formula presented.) concentrations for the next 24 h in a new location where measurements are not available. This work describes the experiments carried out, with particular emphasis on the pre-processing step, which constitutes an important factor for obtaining relatively good results. The proposed multilayer LSTM model obtained (Formula presented.) values equal to 0.74 and 0.38 in seven stations when considering forecasts of 1 and 24 h, respectively. As future work, we plan to include more input variables in the proposed model and to use attention-based networks. © 2022 Elsevier B.V., All rights reserved.
dc.description.ia_keywordformula, presented, concentrations, model, prediction, santiago, chile
dc.formatPDF
dc.identifier.urihttps://repositoriodigital.uct.cl/handle/10925/6746
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relationinstname: ANID
dc.relationreponame: Repositorio Digital RI2.0
dc.rights.driverinfo:eu-repo/semantics/openAccess
dc.sourceApplied Sciences (Switzerland)
dc.subject.ia_odsODS 3: Salud y bienestar
dc.subject.ia_oecd1nCiencias Naturales
dc.subject.ia_oecd2nCiencias Biológicas
dc.subject.ia_oecd3nCiencias del Medio Ambiente
dc.type.driverinfo:eu-repo/semantics/article
dc.type.driverhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.type.openaireinfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublication
oaire.citationEdition2022
oaire.citationIssue22
oaire.citationTitleApplied Sciences (Switzerland)
oaire.citationVolume12
oaire.fundingReferenceANID BASAL FB210017 (CENIA)
oaire.fundingReferenceANID FONDAP 15110017 (CIGIDEN)
oaire.fundingReferenceANID FONDECYT 1201478
oaire.licenseConditionObra bajo licencia Creative Commons Atribución 4.0 Internacional
oaire.licenseCondition.urihttps://creativecommons.org/licenses/by/4.0/
oaire.resourceTypeArtículo
oaire.resourceType.enArticle
uct.catalogadorjvu
uct.comunidadIngenieríaen_US
uct.departamentoDepartamento de Ingeniería Informática
uct.facultadFacultad de Ingeniería
uct.indizacionScience Citation Index Expanded - SCIE
uct.indizacionScopus
uct.indizacionEi Compendex
uct.indizacionScimago
uct.indizacionDOAJ
uct.indizacionCrossref
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