A hybrid CNN-RNN model for particulate matter forecasting in Seoul

Title : A hybrid CNN-RNN model for particulate matter forecasting in Seoul
Published in : The 14th Asia Pacific International Conference on Information Science and Technology (APIC-IST 2019)
Author : Guang Yang, Thanongsak Xayasouk, HwaMin Lee
Corresponding author : HwaMin Lee
Location : Grand Gongda JianGuo Hotel, Beijing, China

Epidemiological research shows that short-term exposure to air pollution especially particulate matter can damage human health. A reliable predicting model with high accuracy is required. Contribute to building real-time forecasting and alarm system. Previous papers focus on improving accuracy by optimizing a single statistical predicting method. In this paper, a hybrid deep learning model is proposed to predict the future day’s particulate matter concentration in Seoul, South Korea. The proposed model combined RNN with a pre-trained ConvNet to forecast future PM2.5 and PM10 concentrations. CNN used to extract the hidden features and used RNN consisted of the gated recurrent unit to make a prediction. With combine the meteorological data, the proposed model can achieve satisfying accuracy and hold less time to consume compared with the bare RNN network.

This research supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program (IITP-2019-2015-0-00403) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation) and Basic Science Research Program through the National Research Foundation of Korea (NRF-2017R1A2B4010570).