TY  - EJOU
AU  - Oh, Byoung-Doo 
AU  - Song, Hye-Jeong 
AU  - Kim, Jong-Dae 
AU  - Park, Chan-Young 
AU  - Kim, Yu-Seop 

TI  - Predicting Concentration of PM10 Using Optimal Parameters of Deep  Neural Network
T2  - Intelligent Automation \& Soft Computing

PY  - 2019
VL  - 25
IS  - 2
SN  - 2326-005X

AB  - Accurate prediction of fine dust (PM10) concentration is currently recognized as 
an important problem in East Asia. In this paper, we try to predict the 
concentration of PM10 using Deep Neural Network (DNN). Meteorological 
factors, yellow dust (sand), fog, and PM10 are used as input data. We test two 
cases. The first case predicts the concentration of PM10 on the next day using 
the day’s weather forecast data. The second case predicts the concentration of 
PM10 on the next day using the previous day’s data. Based on this, we compare 
the various performance results from the DNN model. In the experiments, we 
get about 76% of accuracy with the proposed system.
KW  - Deep Neural Network (DNN); PM10; classification

DO  - 10.31209/2019.100000095