A Machine Learning Approach to Predict Air Quality in California
Predicting air quality is a complex task due to the dynamic nature, volatility, and high variability in time and space of pollutants and particulates. At the same time, being able to model, predict, and monitor air quality is becoming more and more relevant, especially in urban areas, due to the obs...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8049504 |
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| author | Mauro Castelli Fabiana Martins Clemente Aleš Popovič Sara Silva Leonardo Vanneschi |
| author_facet | Mauro Castelli Fabiana Martins Clemente Aleš Popovič Sara Silva Leonardo Vanneschi |
| author_sort | Mauro Castelli |
| collection | DOAJ |
| description | Predicting air quality is a complex task due to the dynamic nature, volatility, and high variability in time and space of pollutants and particulates. At the same time, being able to model, predict, and monitor air quality is becoming more and more relevant, especially in urban areas, due to the observed critical impact of air pollution on citizens’ health and the environment. In this paper, we employ a popular machine learning method, support vector regression (SVR), to forecast pollutant and particulate levels and to predict the air quality index (AQI). Among the various tested alternatives, radial basis function (RBF) was the type of kernel that allowed SVR to obtain the most accurate predictions. Using the whole set of available variables revealed a more successful strategy than selecting features using principal component analysis. The presented results demonstrate that SVR with RBF kernel allows us to accurately predict hourly pollutant concentrations, like carbon monoxide, sulfur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, as well as the hourly AQI for the state of California. Classification into six AQI categories defined by the US Environmental Protection Agency was performed with an accuracy of 94.1% on unseen validation data. |
| format | Article |
| id | doaj-art-1ff27e737e5148278029c2833d6a601d |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-1ff27e737e5148278029c2833d6a601d2025-02-03T01:05:08ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/80495048049504A Machine Learning Approach to Predict Air Quality in CaliforniaMauro Castelli0Fabiana Martins Clemente1Aleš Popovič2Sara Silva3Leonardo Vanneschi4NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, Lisboa 1070-312, PortugalNOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, Lisboa 1070-312, PortugalNOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, Lisboa 1070-312, PortugalLASIGE, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Lisboa 1749-016, PortugalNOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, Lisboa 1070-312, PortugalPredicting air quality is a complex task due to the dynamic nature, volatility, and high variability in time and space of pollutants and particulates. At the same time, being able to model, predict, and monitor air quality is becoming more and more relevant, especially in urban areas, due to the observed critical impact of air pollution on citizens’ health and the environment. In this paper, we employ a popular machine learning method, support vector regression (SVR), to forecast pollutant and particulate levels and to predict the air quality index (AQI). Among the various tested alternatives, radial basis function (RBF) was the type of kernel that allowed SVR to obtain the most accurate predictions. Using the whole set of available variables revealed a more successful strategy than selecting features using principal component analysis. The presented results demonstrate that SVR with RBF kernel allows us to accurately predict hourly pollutant concentrations, like carbon monoxide, sulfur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, as well as the hourly AQI for the state of California. Classification into six AQI categories defined by the US Environmental Protection Agency was performed with an accuracy of 94.1% on unseen validation data.http://dx.doi.org/10.1155/2020/8049504 |
| spellingShingle | Mauro Castelli Fabiana Martins Clemente Aleš Popovič Sara Silva Leonardo Vanneschi A Machine Learning Approach to Predict Air Quality in California Complexity |
| title | A Machine Learning Approach to Predict Air Quality in California |
| title_full | A Machine Learning Approach to Predict Air Quality in California |
| title_fullStr | A Machine Learning Approach to Predict Air Quality in California |
| title_full_unstemmed | A Machine Learning Approach to Predict Air Quality in California |
| title_short | A Machine Learning Approach to Predict Air Quality in California |
| title_sort | machine learning approach to predict air quality in california |
| url | http://dx.doi.org/10.1155/2020/8049504 |
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