PM

Backward elimination Genetic algorithm Modified depth-first search (DFS) Multilayer perceptron neural network (MLP) Particulate matter (PM(10)) forecast

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:
20 Jul 2020
Historique:
received: 22 01 2020
revised: 24 03 2020
accepted: 04 04 2020
pubmed: 26 4 2020
medline: 26 4 2020
entrez: 26 4 2020
Statut: ppublish

Résumé

Atmospheric particulate matter (PM) is an important factor that influences the weather and climate changes which have an impact on life and Earth. In this study, we attempt to forecast PM

Identifiants

pubmed: 32334215
pii: S0048-9697(20)32020-9
doi: 10.1016/j.scitotenv.2020.138507
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

138507

Informations de copyright

Copyright © 2018 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest We have no conflict of interest statement.

Auteurs

Chadaphim Photphanloet (C)

Machine Intelligence and Multimedia Information Technology Laboratory (MIMIT Lab), Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Thailand. Electronic address: chadaphim.Ph@student.chula.ac.th.

Rajalida Lipikorn (R)

Machine Intelligence and Multimedia Information Technology Laboratory (MIMIT Lab), Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Thailand. Electronic address: rajalida.l@chula.ac.th.

Classifications MeSH