Spatial prediction of PM10 concentration using machine learning algorithms in Ankara, Turkey

• Published in: Environmental pollution (1987) 2020-08, Vol.263, p.114635, Article 114635
• Main Authors: Bozdağ, Aslı, Dokuz, Yeşim, Gökçek, Öznur Begüm
• Format: Article
• Language: English
• Subjects: Air pollution parameter; Artificial intelligence; Machine learning; Predictive modeling; Spatial distribution
• ISSN: 0269-7491; 1873-6424
• DOI: 10.1016/j.envpol.2020.114635

With the increase in population and industrialization, air pollution has become one of the global problems nowadays. Therefore, air pollutant parameters should be measured at regular intervals, and the necessary measures should be taken by evaluating the results of measurements. In order to prevent air pollution, pollutant parameters must be evaluated within the framework of a model. Recently, in order to obtain objective and more sensitive results with regard to air pollution nowadays, studies, which use machine learning algorithms in artificial intelligence technologies, have been carried out. In this study, PM10 concentrations, which are obtained from 7 stations in Ankara province in Turkey, were trained with machine learning algorithms (LASSO, SVR, RF, kNN, xGBoost, ANN). The PM10 concentrations of the years 2009–2017 of 6 stations in Ankara were given as input, and the PM10 concentrations of the seventh station for the year 2018 were predicted. The model development stage was repeated for each station, and the performance and error rates of the algorithms were determined by comparing the results produced by the algorithms with the actual results. The best results were provided with ANN (R2 = 0.58, RMSE = 20.8, MAE = 14.4). The spatial distribution of the estimated concentration results was provided through Geographic Information System (GIS), and spatial strategies for improving air pollution over land use were established. [Display omitted] •The PM10 concentrations of the years 2009–2017 of 6 stations in Ankara of Turkey were given as input, and the PM10 concentrations of the seventh station for the year 2018 were predicted with machine learning algorithms (LASSO, SVR, RF, kNN, xGBoost, ANN).•The best results were provided with ANN (R2 = 0.58, RMSE = 20.8, MAE = 14.4).•The spatial distribution of the estimated concentration results was provided through GIS, and spatial strategies for improving air pollution over land use were established.