@Article{2019.100000095,
AUTHOR = {Byoung-Doo Oh, Hye-Jeong Song, Jong-Dae Kim, Chan-Young Park, Yu-Seop Kim},
TITLE = {Predicting Concentration of PM10 Using Optimal Parameters of Deep  Neural Network},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {25},
YEAR = {2019},
NUMBER = {2},
PAGES = {343--350},
URL = {http://www.techscience.com/iasc/v25n2/39661},
ISSN = {2326-005X},
ABSTRACT = {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.},
DOI = {10.31209/2019.100000095}
}