A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide.
Fine particles
Land use regression
Machine learning
Nitrogen dioxide
Journal
Environment international
ISSN: 1873-6750
Titre abrégé: Environ Int
Pays: Netherlands
ID NLM: 7807270
Informations de publication
Date de publication:
09 2019
09 2019
Historique:
received:
08
02
2019
revised:
21
05
2019
accepted:
13
06
2019
pubmed:
24
6
2019
medline:
26
2
2020
entrez:
24
6
2019
Statut:
ppublish
Résumé
Empirical spatial air pollution models have been applied extensively to assess exposure in epidemiological studies with increasingly sophisticated and complex statistical algorithms beyond ordinary linear regression. However, different algorithms have rarely been compared in terms of their predictive ability. This study compared 16 algorithms to predict annual average fine particle (PM
Identifiants
pubmed: 31229871
pii: S0160-4120(19)30440-4
doi: 10.1016/j.envint.2019.104934
pii:
doi:
Substances chimiques
Air Pollutants
0
Particulate Matter
0
Nitrogen Dioxide
S7G510RUBH
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
104934Subventions
Organisme : Medical Research Council
ID : MR/S019669/1
Pays : United Kingdom
Informations de copyright
Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.