Particulate Matter forecasting using RNN with LSTM

Title : Particulate Matter forecasting using RNN with LSTM
Published in : International Research Conference on Innovation, Technology and Sustainability (IRCITS 2019)
Author : Guang Yang, Minsu Chae, HwaMin Lee
Corresponding author : HwaMin Lee
Location : Century Park Hotel, Manila, Philippines

Abstract
The particulate matter especially PM2.5 can cause respiratory, cardiovascular and nervous system damage as many studies prove. With the increasing concentration of PM in many countries, the prediction of PM became an important issue. In this paper, we tried to create ann recurrent neural network (RNN) with long short-term memory(LSTM) to predict the concentrations of PM2.5 and PM10 7 days in advance in several cities and areas in South Korea.

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).