:: Volume 3, Issue 4 (3-2016) ::
jgit 2016, 3(4): 43-63 Back to browse issues page
An Online Approach for Spatio-Temporal Prediction of Air Pollution in Tehran using Support Vector Machine
Zeinab Ghaemi *, Mahdi Farnaghi, Abbas Alimohammadi
K.N.Toosi University of Technology
Abstract:   (3961 Views)

Due to its critical impact on human health and the environment, monitoring and prediction of air pollution have become an important issue during the past decades. Non-linear behavior of air pollution in one hand and high volume of required data on the other hand, intensifies the complexity of air pollution prediction especially in online applications. In order to overcome the deficiencies of traditional methods, this study proposes an online algorithm based on Support Vector Machine (SVM) to predict the time series of air pollution in the city of Tehran, Iran. Prediction is performed on the basis of time series data of pollutant concentrations, weather condition, and geographical parameters such as traffic, surface curvature and local altitude. Evaluation of the outputs shows that prediction errors are within an acceptable range and the online algorithm has an outstanding speed in comparison with the conventional SVM. The overall accuracy of 0.71, RMSE of 0.54 and R2 of 0.81 prove the efficiency of the proposed algorithm to develop a system to dynamically predict Tehran’s air pollution in advance.

Keywords: Online air pollution prediction, Support Vector Machine, Time series, Geographic Information System, Big data.
Full-Text [PDF 1341 kb]   (2078 Downloads)    
Type of Study: Research |
Received: 2016/07/3 | Accepted: 2016/07/3 | Published: 2016/07/3

XML   Persian Abstract   Print

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 3, Issue 4 (3-2016) Back to browse issues page