20.500.12556/RUL-118023 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 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. informatics ecology artificial intelligence informatika ekologija umetna inteligenca true false false Angleški jezik Slovenski jezik Članek v reviji 2020-08-13 15:32:15 2020-08-13 15:34:54 2022-08-30 04:01:05 0000-00-00 00:00:00 2020 0 0 [in press] 2020 0000-00-00 Zaloznikova NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 659.2:004 1076-2787 10.1155/2020/8049504 24544771 1926171 RAZ_Castelli_Mauro_2020.pdf RAZ_Castelli_Mauro_2020.pdf 1 D5CE63562DB8E1D18E69F2F60E67D3A4 013ed5763edffe664c3ba1edcb6371cfe726d116aba0f3a6d1390e8d186fbcd8 4c632a94-a1b9-11eb-a523-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=131956 Ekonomska fakulteta 0 0 0