CNN and LSTM models for predicting particulate matter in the Republic of Korea

Title : CNN and LSTM models for predicting particulate matter in the Republic of Korea
Published in : The 8th International Conference on Information, System and Convergence Applications International Symposium on Innovation in Information Technology and Application 2020 (ICISCA 2020)
Author : Minsu Chae, Sangwook Han, HwaMin Lee
Corresponding author : HwaMin Lee
Location : Ton Duc Thang University, Ho Chi Minh, Vietnam and Online

Abstract
Recently, fine dust has become a problem all over the world. Many countries set regulations for PM10 and PM2.5. Because fine dust adversely affects human health, it is important to alert PM10 and PM2.5 in each city. Predictions for PM10 and PM2.5 are required to alert. We propose a hybrid model of the CNN(Convolutional Neural Network) model and the LSTM(Long Short-Term Memory) model to predict the PM10 and PM2.5 in the Republic of Korea. In addition, we evaluate the performance of the CNN model, the LSTM model, and the proposed hybrid model. As a result of the performance evaluation, the proposed hybrid model has 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).