Air pollution prediction system using deep learning

Abstract

One of the most influential factors on human health is air pollution, such as the concentration of PM10 and PM2.5 is a damage to a human. Despite the growing interest in air pollution in Korea, it is difficult to obtain accurate information due to the lack of air pollution measuring stations at the place where the user is located. Deep learning is a type of machine learning method has drawn a lot of academic and industrial interest. In this paper, we proposed a deep learning approach for the air pollution prediction in South Korea. We use Stacked Autoencoders model for learning and training data. The experiment results show the performance of the air pollution prediction system and model that proposed.