Forecasting Air Pollution Particulate Matter (PM2.5) Using Machine Learning Regression Models

• Published in: Procedia computer science 2020, Vol.171, p.2057-2066
• Main Authors: Doreswamy, K S, Harishkumar, KM, Yogesh, Gad, Ibrahim
• Format: Article
• Language: English
• Subjects: Air Pollution; Forecasting; Gradient Boosting regression; Particulate matter (PM2.5); Prediction; TAQMN
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2020.04.221

From the past few decades, it has been observed that the urbanization and industrialization are expanding in the developed nations and are confronting the overwhelming air contamination issue. The citizens and governments have experienced and expressed the increasingly concerned regarding the impact of air pollution affecting human health and proposed sustainable development for overriding air pollution issues across the worldwide. The outcome of modern industrialization contains the liquid droplets, solid particles and gas molecules and is spreading in the atmospheric air. The heavy concentration of particulate matter of size PM10 and PM2.5 is seriously caused adverse health effect. Through the determination of particulate matter concentration in atmospheric air for the betterment of human being well in primary importance. In this paper machine learning predictive models for forecasting particulate matter concentration in atmospheric air are investigated on Taiwan Air Quality Monitoring data sets, which were obtained from 2012 to 2017. These models were compared with the existing traditional models and perform better in predictive performance. The performance of these models was evaluated with statistical measures: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE), and Coefficient of Determination (R2).