Machine learning algorithms to forecast air quality: a survey

被引：110

作者：

Mendez, Manuel ^{[1
]}

Merayo, Mercedes G. ^{[1
]}

Nunez, Manuel ^{[1
]}

机构：

[1] Univ Complutense Madrid, Design & Testing Reliable Syst Res Grp, C Prof Jose Garcia St esmases 9, Madrid 28040, Madrid, Spain

来源：

ARTIFICIAL INTELLIGENCE REVIEW | 2023年 / 56卷 / 09期

关键词：

Machine learning; Deep learning; Regression algorithms; Air quality; NEURAL-NETWORK; PREDICTION; PM2.5; MODEL; ENSEMBLE; INTERPOLATION; SCALE;

D O I：

10.1007/s10462-023-10424-4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：10031 / 10066

页数：36

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