A machine learning approach to predict air quality in California

Castelli, Mauro (author), Martins Clemente, Fabiana (author), Popovič, Aleš (author), Silva, Sara (author), Vanneschi, Leonardo (author)

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.

• Language: English
• Keywords: informatics, ecology, artificial intelligence
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: EF - School of Economics and Business
• Publication version: Version of Record
• Year: 2020
• PID: 20.500.12556/RUL-118023
• UDC: 659.2:004
• ISSN on article: 1076-2787
• DOI: 10.1155/2020/8049504
• COBISS.SI-ID: 24544771
• Publication date in RUL: 13.08.2020

Record is a part of a journal

• Title: Complexity
• Shortened title: Complexity
• Publisher: Wiley & Sons
• ISSN: 1076-2787
• COBISS.SI-ID: 1926171

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 13.08.2020

Secondary language

• Language: Slovenian
• Keywords: informatika, ekologija, umetna inteligenca

Projects

• Funder: ARRS - Slovenian Research Agency
• Project number: P5-0410
• Name: Digitalizacija kot gonilo trajnostnega razvoja posameznika, organizacij in družbe