Comparative Study of Spatial Prediction Models for Estimating PM2.5 Concentration Level in Urban Areas

Descripción del Articulo

Acknowledgment. The authors gratefully acknowledge financial support by Fondo Nacional de Desarrollo Científico, Tecnológico y de Innovación Tecnológica (Fondecyt) -Mundial Bank (Grant: 50-2018-FONDECYT-BM-IADT-MU).
Detalles Bibliográficos
Autores: Vargas-Campos I.R., Villanueva E.
Formato: objeto de conferencia
Fecha de Publicación:2021
Institución:Consejo Nacional de Ciencia Tecnología e Innovación
Repositorio:CONCYTEC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.concytec.gob.pe:20.500.12390/2985
Enlace del recurso:https://hdl.handle.net/20.500.12390/2985
https://doi.org/10.1007/978-3-030-76228-5_12
Nivel de acceso:acceso abierto
Materia:Spatial prediction
Air quality
Machine learning
PM2.5
https://purl.org/pe-repo/ocde/ford#2.03.04
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oai_identifier_str oai:repositorio.concytec.gob.pe:20.500.12390/2985
network_acronym_str CONC
network_name_str CONCYTEC-Institucional
repository_id_str 4689
dc.title.none.fl_str_mv Comparative Study of Spatial Prediction Models for Estimating PM2.5 Concentration Level in Urban Areas
title Comparative Study of Spatial Prediction Models for Estimating PM2.5 Concentration Level in Urban Areas
spellingShingle Comparative Study of Spatial Prediction Models for Estimating PM2.5 Concentration Level in Urban Areas
Vargas-Campos I.R.
Spatial prediction
Air quality
Machine learning
PM2.5
https://purl.org/pe-repo/ocde/ford#2.03.04
title_short Comparative Study of Spatial Prediction Models for Estimating PM2.5 Concentration Level in Urban Areas
title_full Comparative Study of Spatial Prediction Models for Estimating PM2.5 Concentration Level in Urban Areas
title_fullStr Comparative Study of Spatial Prediction Models for Estimating PM2.5 Concentration Level in Urban Areas
title_full_unstemmed Comparative Study of Spatial Prediction Models for Estimating PM2.5 Concentration Level in Urban Areas
title_sort Comparative Study of Spatial Prediction Models for Estimating PM2.5 Concentration Level in Urban Areas
author Vargas-Campos I.R.
author_facet Vargas-Campos I.R.
Villanueva E.
author_role author
author2 Villanueva E.
author2_role author
dc.contributor.author.fl_str_mv Vargas-Campos I.R.
Villanueva E.
dc.subject.none.fl_str_mv Spatial prediction
topic Spatial prediction
Air quality
Machine learning
PM2.5
https://purl.org/pe-repo/ocde/ford#2.03.04
dc.subject.es_PE.fl_str_mv Air quality
Machine learning
PM2.5
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#2.03.04
description Acknowledgment. The authors gratefully acknowledge financial support by Fondo Nacional de Desarrollo Científico, Tecnológico y de Innovación Tecnológica (Fondecyt) -Mundial Bank (Grant: 50-2018-FONDECYT-BM-IADT-MU).
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.available.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.issued.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12390/2985
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1007/978-3-030-76228-5_12
dc.identifier.scopus.none.fl_str_mv 2-s2.0-85111111277
url https://hdl.handle.net/20.500.12390/2985
https://doi.org/10.1007/978-3-030-76228-5_12
identifier_str_mv 2-s2.0-85111111277
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.none.fl_str_mv Communications in Computer and Information Science
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Springer Science and Business Media Deutschland GmbH
publisher.none.fl_str_mv Springer Science and Business Media Deutschland GmbH
dc.source.none.fl_str_mv reponame:CONCYTEC-Institucional
instname:Consejo Nacional de Ciencia Tecnología e Innovación
instacron:CONCYTEC
instname_str Consejo Nacional de Ciencia Tecnología e Innovación
instacron_str CONCYTEC
institution CONCYTEC
reponame_str CONCYTEC-Institucional
collection CONCYTEC-Institucional
repository.name.fl_str_mv Repositorio Institucional CONCYTEC
repository.mail.fl_str_mv repositorio@concytec.gob.pe
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spelling Publicationrp08462600rp08463600Vargas-Campos I.R.Villanueva E.2024-05-30T23:13:38Z2024-05-30T23:13:38Z2021https://hdl.handle.net/20.500.12390/2985https://doi.org/10.1007/978-3-030-76228-5_122-s2.0-85111111277Acknowledgment. The authors gratefully acknowledge financial support by Fondo Nacional de Desarrollo Científico, Tecnológico y de Innovación Tecnológica (Fondecyt) -Mundial Bank (Grant: 50-2018-FONDECYT-BM-IADT-MU).Having accurate spatial prediction models of air pollutant concentrations can be very helpful to alleviate the shortage of monitoring stations, specially in low-to-middle income countries. However, given the large diversity of model types, both statistical, numerical and machine learning (ML) based, it is not clear which of them are most suitable for this task. In this paper we study the predictive capabilities of common machine learning methods for the spatial prediction of PM2.5 concentration level. Three relevant factors were scrutinized: the extent to which meteorological variables impact the prediction performance; the effect of variable normalization by inverse distance weighting (IDW); and the number of neighborhood stations needed to maximize predictive performance. Results in a dataset from Beijing monitoring network show that simple models like Linear Regresors trained on IDW normalized variables can cope with this task. Some knowledge have been derived to guide the construction of competent models for spatial prediction of PM2.5 concentrations with ML-based methods. © 2021, Springer Nature Switzerland AG.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecengSpringer Science and Business Media Deutschland GmbHCommunications in Computer and Information Scienceinfo:eu-repo/semantics/openAccessSpatial predictionAir quality-1Machine learning-1PM2.5-1https://purl.org/pe-repo/ocde/ford#2.03.04-1Comparative Study of Spatial Prediction Models for Estimating PM2.5 Concentration Level in Urban Areasinfo:eu-repo/semantics/conferenceObjectreponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC20.500.12390/2985oai:repositorio.concytec.gob.pe:20.500.12390/29852024-05-30 16:12:50.354http://purl.org/coar/access_right/c_14cbinfo:eu-repo/semantics/closedAccessmetadata only accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="f30bb911-e858-4ae8-a335-1da038362c65"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Comparative Study of Spatial Prediction Models for Estimating PM2.5 Concentration Level in Urban Areas</Title> <PublishedIn> <Publication> <Title>Communications in Computer and Information Science</Title> </Publication> </PublishedIn> <PublicationDate>2021</PublicationDate> <DOI>https://doi.org/10.1007/978-3-030-76228-5_12</DOI> <SCP-Number>2-s2.0-85111111277</SCP-Number> <Authors> <Author> <DisplayName>Vargas-Campos I.R.</DisplayName> <Person id="rp08462" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Villanueva E.</DisplayName> <Person id="rp08463" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Springer Science and Business Media Deutschland GmbH</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>Spatial prediction</Keyword> <Keyword>Air quality</Keyword> <Keyword>Machine learning</Keyword> <Keyword>PM2.5</Keyword> <Abstract>Having accurate spatial prediction models of air pollutant concentrations can be very helpful to alleviate the shortage of monitoring stations, specially in low-to-middle income countries. However, given the large diversity of model types, both statistical, numerical and machine learning (ML) based, it is not clear which of them are most suitable for this task. In this paper we study the predictive capabilities of common machine learning methods for the spatial prediction of PM2.5 concentration level. Three relevant factors were scrutinized: the extent to which meteorological variables impact the prediction performance; the effect of variable normalization by inverse distance weighting (IDW); and the number of neighborhood stations needed to maximize predictive performance. Results in a dataset from Beijing monitoring network show that simple models like Linear Regresors trained on IDW normalized variables can cope with this task. Some knowledge have been derived to guide the construction of competent models for spatial prediction of PM2.5 concentrations with ML-based methods. © 2021, Springer Nature Switzerland AG.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1
score 13.394457
Comparative Study of Spatial Prediction Models for Estimating PM2.5 Concentration Level in Urban Areas
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