Particulate Matter Prediction Using LSTM and GRU

Title : Particulate Matter Prediction Using LSTM and GRU
Published in : The 2019 World Congress on Information Technology Applications and Services (World IT Congress 2019)
Author : Guang Yang, Thanongsak Xayasouk, HwaMin Lee
Corresponding author : HwaMin Lee
Location : Jeju National University, Jeju, Korea

Abstract
Particulate matters (PM) proved can cause cardiovascular, respiratory and nervous system damage, especially PM2.5. Many types of research contributed to making high accuracy prediction of PM concentrations. Machine learning as one of a powerful tool for prediction, have many types of research make a prediction with machine learning models. In this paper, we implemented a prediction system to predict several future days PM10 and PM2.5 concentration with a recurrent neural network (RNN). Consisted of LSTM and GRU units. We predicted several main areas of South Korea and the experiment shows the proposed models can give more than 10 days prediction with high 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).