Byoung-Doo Oha,b, Hye-Jeong Songa,b, Jong-Dae Kima,b, Chan-Young Parka,b, Yu-Seop Kima,b
Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 343-350, 2019, DOI:10.31209/2019.100000095
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… More >
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