Prediction of air pollution and analysis of its effects on the pollution dispersion of PM 10 in Egypt using machine learning algorithms

Air pollution has been considered as one of the serious threats in Egypt. According to a study in Environmental Science & Technology Letters journal, air pollution is one of the main responsible for shortening Egyptians lives by 1.85 years. The main cause of air pollution in Egypt is PM10 which comes from industrial processes. PM10 concentrations exceed daily average concentrations during 98% of the measurement period. In this paper, we will apply machine learning classification algorithms to build the most accurate model for air pollution prediction and analysing its effects on pollution dispersion of PM10. The proposed classification model begins with air quality data collection and pre-processing, and then classifying process to discover the main relevant features for prediction. Experimental results show a good performance of the proposed air quality model. Random forest and naïve Bayes algorithms achieved accuracy almost 82%, and JRip and fuzzy classifier achieved less classification results accuracy 65%, 76% respectively.