Open Access

ARTICLE

Predicting Concentration of PM10 Using Optimal Parameters of Deep Neural Network

Byoung-Doo Oh^a,b, Hye-Jeong Song^a,b, Jong-Dae Kim^a,b, Chan-Young Park^a,b, Yu-Seop Kim^a,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. https://doi.org/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 experiments, we get about 76% of accuracy with the proposed system.

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Keywords

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