Machine learning algorithms to forecast air quality: a survey

Air pollution is a risk factor for many diseases that can lead to death. Therefore, it is important to develop forecasting mechanisms that can be used by the authorities, so that they can anticipate measures when high concentrations of certain pollutants are expected in the near future. Machine Lear...

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Bibliographic Details
Published in:The Artificial intelligence review Vol. 56; no. 9; pp. 10031 - 10066
Main Authors: Méndez, Manuel, Merayo, Mercedes G., Núñez, Manuel
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.09.2023
Springer
Springer Nature B.V
Subjects:
Air pollution
Air quality
Algorithms
Artificial Intelligence
Computer Science
Data mining
Deep learning
Forecasting
Geographical distribution
Machine learning
Mathematical models
Neural networks
Outdoor air quality
Risk factors
Surveys
Deep learning
Air quality
Regression algorithms
Machine learning
ISSN:0269-2821, 1573-7462
Online Access:Get full text
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Summary:Air pollution is a risk factor for many diseases that can lead to death. Therefore, it is important to develop forecasting mechanisms that can be used by the authorities, so that they can anticipate measures when high concentrations of certain pollutants are expected in the near future. Machine Learning models, in particular, Deep Learning models, have been widely used to forecast air quality. In this paper we present a comprehensive review of the main contributions in the field during the period 2011–2021. We have searched the main scientific publications databases and, after a careful selection, we have considered a total of 155 papers. The papers are classified in terms of geographical distribution, predicted values, predictor variables, evaluation metrics and Machine Learning model.
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ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-023-10424-4