Description of Invention
Air pollution in smart cities of Malaysia has been drastic lately. An increase in the concentration of particulate matter (PM2.5) in the air is a threat for the country and citizens as it can out-turn unbearable consequences. This study aims to implement machine learning algorithms to find the accuracy of the prediction of PM2.5 in air pollution in smart cities of Malaysia. To test the implementation of machine learning in this prediction, Artificial Neural Network (ANN), Random Forest (RF) and Long Short-Term Memory (LSTM) are chosen for the experiment. RF outperformed with an accuracy of 97.7% accuracy.