LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran

Due to critical impacts of air pollution, prediction andmonitoring of air quality in urban areas are important tasks. However, because of the dynamic nature and high spatio-temporal variability, prediction of the air pollutant concentrations is a complex spatio-temporal problem. Distribution of pollutant concentration is influenced by various factors such as the historical pollution data and weather conditions. Conventional methods such as the support vectormachine (SVM) or artificial neuralnetworks (ANN) show some deficiencies when huge amount of streaming data have to be analyzed for urban air pollution prediction. In order to overcome the limitations of the conventional methods and improve the performance of urban air pollution prediction in Tehran, a spatio-temporal systemisdesignedusingaLaSVM-basedonline algorithm. Pollutant concentration andmeteorological data along with geographical parameters are continually fed to the developed online forecasting system. Performance of the system is evaluated by comparing the prediction results of theAir QualityIndex(AQI) with thoseof a traditionalSVM algorithm. Results show an outstanding increase of speed bytheonline algorithmwhile preserving the accuracyof the SVM classifier. Comparison of the hourly predictions for next coming24 h, with those of the measured pollution data in Tehran pollution monitoring stations shows an overall accuracy of 0.71, root mean square error of 0.54 and coefficient of determination of 0.81. These results are indicators of the practical usefulness of the online algorithm for realtimespatial and temporal prediction of the urban air quality.