Air quality forecasting using artificial neural networks with real time dynamic error correction in highly polluted regions.
Air pollution forecasting
Artificial neural network
Delhi pollution
Pollution prediction
Real-time correction
Journal
The Science of the total environment
ISSN: 1879-1026
Titre abrégé: Sci Total Environ
Pays: Netherlands
ID NLM: 0330500
Informations de publication
Date de publication:
15 Sep 2020
15 Sep 2020
Historique:
received:
01
04
2020
revised:
11
05
2020
accepted:
13
05
2020
pubmed:
3
6
2020
medline:
3
6
2020
entrez:
3
6
2020
Statut:
ppublish
Résumé
Air pollution is an important issue, especially in megacities across the world. There are emission sources within and also in the regions around these cities, which cause fluctuations in air quality based on prevailing meteorological conditions. Short term air quality forecasting is used not to just possibly mitigate forthcoming high air pollution episodes, but also to plan for reduced exposures of residents. In this study, a model using Artificial Neural Networks (ANN) has been developed to forecast pollutant concentration of PM
Identifiants
pubmed: 32485449
pii: S0048-9697(20)32971-5
doi: 10.1016/j.scitotenv.2020.139454
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
139454Informations de copyright
Copyright © 2020 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.