Title : CNN and RNN models for predicting particulate matter in South Korea
Published in : The 11th International Conference on Internet (ICONI 2019)
Author : Guang Yang, Thanongsak Xayasouk, HwaMin Lee
Corresponding author : HwaMin Lee
Location : Lotte Hotels & Resorts Hanoi, Hanoi, Vietnam
Particulate matter proved to have severe effects on human health. To implement a real-time predicting system for is indispensable for every country face air pollution problem. Based on the previous study, predicting models for local areas are implemented. In this paper, a convolutional network, a recurrent neural network and a hybrid model combines the convolutional layers with the recurrent layers apply to predict future one weeks PM10 and PM2.5 concentration in seven urban cities in South Korea. The main contribution in this paper is: proposed three predicting models, make a comparison of three different models for predicting the concentration of particulate matter, all models are proved capable to obtain reliable predicting results. Experiments show GRU archive best performance for most cities both for PM10 and PM2.5, CNN models speed up the training process with least time consumption. The hybrid model performance stable with less time for training.
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).