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Predicting Concentration of PM10 Using Optimal Parameters of Deep Neural Network

Byoung-Doo Oha,b, Hye-Jeong Songa,b, Jong-Dae Kima,b, Chan-Young Parka,b, Yu-Seop Kima,b

a School of Software, Hallym University, Chuncheon, Korea
b Bio-IT Research Center, Hallym University, Chuncheon, Korea

* Corresponding Author: Yu-Seop Kim,

Intelligent Automation & Soft Computing 2019, 25(2), 343-350.


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.


Cite This Article

B. Oh, H. Song, J. Kim, C. Park and Y. Kim, "Predicting concentration of pm10 using optimal parameters of deep neural network," Intelligent Automation & Soft Computing, vol. 25, no.2, pp. 343–350, 2019.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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