Air Quality Prediction Using Machine Learning Methods: A Case Study of Bjelave Neighborhood, Sarajevo, BiH

Džaferović, Emina; Karađuzović-Hadžiabdić, Kanita

Air pollution is a complex mixture of toxic components that has the direct impact on human health, life quality, and the environment. In this study, meteorological variables and concentration of air pollutants are used to predict the common air quality index (CAQI) in Bjelave neighborhood, Sarajevo, BiH. CAQI prediction models were built using five popular machine learning techniques in the air pollution domain: Support Vector Regression, Random Forest, Extreme Gradient Boosting, Multiple Linear Regression and Multilayer Perceptron, using three-year period data (2016–2018). Prediction performance was measured using regression metrics: R-squared and RMSE. Ensemble technique, Random Forest method achieved the best performance results from the five evaluated machine learning methods: R(2) = 0.99 and RMSE = 2.30, using the dataset when missing values were removed, and R(2) = 0.99 and RMSE = 2.58 using the dataset when missing values were imputed using linear regression method.