AIR POLLUTION PREDICTION SYSTEM USING DEEP LEARNING
Price
Free (open access)
Transaction
Volume
230
Pages
9
Page Range
71 - 79
Published
2018
Paper DOI
10.2495/AIR180071
Copyright
WIT Press
Author(s)
THANONGSAK XAYASOUK, HWAMIN LEE
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.
Keywords
fine dust, PM10, PM2.5, air pollution prediction, deep learning