Air quality prediction using machine learning methods: A case study of Bjelave neighborhood, Sarajevo, BiH

TitleAir quality prediction using machine learning methods: A case study of Bjelave neighborhood, Sarajevo, BiH
Publication TypeConference Paper
Year of Publication2020
Date Published05/2020
Conference NameDays of the Bosnian-Herzegovinian American Academy of Arts and Sciences (BHAAAS)
Volume142
EditionAdvanced Technologies, Systems, and Applications V
Publication LanguageEnglish
AuthorsDžaferović, E, Karađuzović-Hadžiabdić, K
PublisherSpringer International Publishing
ISBN Number978-3-030-54765-3
ISSN Number2367-3370
Keywordsair pollution, AQI, Bjelave, machine learning, Random Forest
Abstract

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: R2 = 0.99 and RMSE = 2.30, using the dataset when missing values were removed, and R2 = 0.99 and RMSE = 2.58 using the dataset when missing values were imputed using linear regression method.

DOI10.1007/978-3-030-54765-3
Refereed DesignationUnknown