Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review
Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review
About this item
Full title
Author / Creator
Publisher
Basel: MDPI AG
Journal title
Language
English
Formats
Publication information
Publisher
Basel: MDPI AG
Subjects
More information
Scope and Contents
Contents
Current studies show that traditional deterministic models tend to struggle to capture the non-linear relationship between the concentration of air pollutants and their sources of emission and dispersion. To tackle such a limitation, the most promising approach is to use statistical models based on machine learning techniques. Nevertheless, it is p...
Alternative Titles
Full title
Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_fffddab97ccf49598395ab774847bc67
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_fffddab97ccf49598395ab774847bc67
Other Identifiers
ISSN
2076-3417
E-ISSN
2076-3417
DOI
10.3390/app8122570
