Evaluating the Performance of SVM and Random Forest in Air Quality Monitoring and Prediction

Air pollution is becoming a major threat in Indian cities and people are facing serious consequences due to air pollution. It affects the environment and the living standard of an individual. Major Pollutants are, particulate matter PM 10, PM 2.5, SO2, CO, NO2, O3, NH3, Pb, Ni, As, Benzo(a)pyrene and Benzene. Inhaling these hazardous substances leads to severe health issues. Predicting the air pollution of a particular area or city can help the government to take appropriate measures to reduce air pollution. Also, public can prevent themselves, if they receive alerts regarding pollution. Here, Random Forest Algorithm (RFA) and Support Vector Machine (SVM) algorithms are applied for predicting the pollutants such as PM 2.5, PM 10, CO, SO2. The performance is evaluated by measuring the Root Mean Square Error (RMSE). Prediction graphs show that the true value and the estimated value made by the models are close to each other which shows the accuracy of the algorithms. It was successfully demonstrated that error for SVM with non-linear kernal was 0.92 and for Random Forest algorithm the error was 0.91. The results prove that machine-learning algorithms can be utilized effectively to predict the AQI.