Smart City Air Quality Prediction using Machine Learning

Air pollution in smart cities in the world has been drastically increasing lately and the increase in the concentration of particulate matter (PM 2.5 ) in the air is a threat for the country and citizens as it can out-turn unbearable consequences such as cardiovascular disease and worsen asthma. PM 2.5 is a deadly air pollutant that is a mixture of solid and liquid coarse particles and has a diameter of 2.5 micrometres. In Malaysia, traffic congestion has been the main contributor to developing air pollution in smart cities such as Kuala Lumpur and Johor Bahru. The systemic way of air pollution prediction using machine learning has been widely studied globally over the years and many machine learning algorithms were studied and tested to find the solution to air pollution in their country. However, very few approaches were done in Malaysia to predict air pollution using machine learning methods. This study aims to implement machine learning algorithms to find the accuracy of the prediction of particulate matter, PM 2.5 in air pollution in smart cities of Malaysia. To test the implementation of machine learning in this prediction, Multi-Layer Perceptron (MLP), and Random Forest are chosen and compared between these two algorithms using the Malaysia Air Pollution dataset. The outcome of this research is that Random Forest gave the best accuracy in prediction of Particulate Matter, PM 2.5 Air Pollution Index in smart cities of Malaysia than MLP