Air Pollution Prediction using Machine Learning Algorithms
Hanan Aljuaid1, Norah Alwabel2
1Hanan Aljuaid, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
2Norah Alwabel, 2Princess Nourah bint Abdulrahman University Riyadh, Saudi Arabia.
Manuscript received on 27 September 2019 | Revised Manuscript received on 09 November 2019 | Manuscript Published on 22 November 2019 | PP: 160-164 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F10260986S319/19©BEIESP | DOI: 10.35940/ijeat.F1026.0986S319
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Air pollution has a serious impact on human health. It occurs because of natural and man-made factors. The major contribution of this research is that it provides a comparison between different methodologies and techniques of mathematical and machine learning models. The process began with integrating data from different sources at different time interval. The preprocessing phase resulted in two different datasets: one-hour and five-minute datasets. Next, we established a forecasting model for particulate matter PM2.5, which is one of the most prevalent air pollutants and its concentration affects air quality. Additionally, we completed a multivariate analysis to predict the PM2.5 value and check the effects of other air pollutants, traffic, and weather. The algorithms used are support vector regression, k-nearest neighbors and decision tree models. The results showed that for the one-hour data set, of the three algorithms, support vector regression has the least root-mean-square error (RMSE) and also lowest value in mean absolute error (MAE). Alternatively, for the five-minute dataset, we found that the auto-regression model showed the least RMSE and MAE; however, this model only predicts short-term PM2.5.
Keywords: Air, Pollution, Machine, Learning, Prediction.
Scope of the Article: Machine, Learning