Time Series Prediction of Air Pollutants

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dc.contributor.authorJanković, Radmila
dc.contributor.authorĆosović, Marijana
dc.contributor.authorAmelio, Alessia
dc.date.accessioned2023-10-20T11:10:11Z
dc.date.available2023-10-20T11:10:11Z
dc.date.issued2019
dc.description.abstractPollution levels are highly dependent on the meteorological parameters, as the weather conditions dictate pollution dispersion and concentration. With the rise of global environmental protection initiatives, there is also a need for accurate prediction of pollution levels. This paper presents a time series prediction of NO2 and CO given four meteorological parameters: (i) air pressure, (ii) relative humidity, (iii) average daily temperature, and (iv) wind speed, using a Nonlinear Autoregressive Exogenous (NARX) neural network. The research is a case study of three European countries: (i) Serbia, (ii) Bosnia and Herzegovina, and (iii) Italy, and involves data from 2014 to 2016 for a total of 1096 instances. The results show that the best prediction accuracy is obtained for CO for data regarding Italy and Bosnia and Herzegovina, and for NO2 for data regarding Serbia. Moreover, the best predictor variables of NO2 are air pressure and relative humidity, followed by the wind speed. The best predictor variables of CO are pressure and temperature for Bosnia and Italy, and wind speed for Serbia
dc.identifier.doi10.1109/INFOTEH.2019.8717778
dc.identifier.urihttps://vaseljena.ues.rs.ba/handle/123456789/867
dc.language.isoen
dc.publisherFaculty of Electrical Engineering, University of East Sarajevo
dc.source18th International Symposium INFOTEH-JAHORINA
dc.subjecttime series; air pollution; prediction; artificial neural network; data mining
dc.titleTime Series Prediction of Air Pollutants
dc.typeArticle
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Електротехнички факултет [Конференције] / Faculty of Electrical Engineering [Conference paper]