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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Vydané v:The Artificial intelligence review Ročník 56; číslo 9; s. 10031 - 10066
Hlavní autori: Méndez, Manuel, Merayo, Mercedes G., Núñez, Manuel
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Dordrecht Springer Netherlands 01.09.2023
Springer
Springer Nature B.V
Predmet:
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
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Abstract 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.
AbstractList 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.
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.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.
Audience Academic
Author Merayo, Mercedes G.
Núñez, Manuel
Méndez, Manuel
Author_xml – sequence: 1
  givenname: Manuel
  orcidid: 0000-0001-9808-6401
  surname: Méndez
  fullname: Méndez, Manuel
  email: manumend@ucm.es
  organization: Design and Testing of Reliable Systems Research Group, Universidad Complutense de Madrid
– sequence: 2
  givenname: Mercedes G.
  surname: Merayo
  fullname: Merayo, Mercedes G.
  organization: Design and Testing of Reliable Systems Research Group, Universidad Complutense de Madrid
– sequence: 3
  givenname: Manuel
  surname: Núñez
  fullname: Núñez, Manuel
  organization: Design and Testing of Reliable Systems Research Group, Universidad Complutense de Madrid
BackLink https://www.ncbi.nlm.nih.gov/pubmed/36820441$$D View this record in MEDLINE/PubMed
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Issue 9
Keywords Deep learning
Air quality
Regression algorithms
Machine learning
Language English
License The Author(s) 2023.
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Snippet 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...
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SubjectTerms 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
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Title Machine learning algorithms to forecast air quality: a survey
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