Stacked Autoencoders Model for Fine Particulate Matter (PM10, PM2.5) Prediction

Title : Stacked Autoencoders Model for Fine Particulate Matter (PM10, PM2.5) Prediction
Published in : The 10th International Conference on Internet (ICONI 2018)
Author : Thanongsak Xayasouk, Guang Yang, HwaMin Lee
Corresponding author : HwaMin Lee
Location : Sokha Phnom Penh Hotel, Phnom Penh, Cambodia

Abstract
Nowadays, Fine Particulate matter (PM10, PM2.5) is very harmful to peoples’ health assuming that we know the information about the concentration of fine particulate matter which dangerous for people, it was able to prevent harmful of the fine particulate matter immediately. Deep learning is a type of machine learning method has drawn a lot of academic and industrial interest. This paper presents a deep learning modeling approach, we mostly use the concentrations of particulate matter (PM10, PM2.5) data and meteorological elements in South Korea. We propose a prediction system based on stacked autoencoder model for learning and training data our data. The experimental results, based on Fine Particulate matter and meteorological data, show that the proposed method can achieve great performance in terms of prediction accuracy.

Acknowledgments
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning(NRF-2017R1A2B4010570) and the Ministry of Science and ICT, Korea, under the ITRC(Information Technology Research Center) support program (IITP-2018-2014-0-00720) supervised by the IITP(Institute for Information & communications Technology Promotion).