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http://dx.doi.org/10.6109/jkiice.2020.24.1.8

Separation Prediction Model by Concentration based on Deep Neural Network for Improving PM10 Forecast Accuracy  

Cho, Kyoung-woo (Department of Electrical, Electronics & Communication Engineering, Korea University of Technology and Education(KOREATECH))
Jung, Yong-jin (Department of Electrical, Electronics & Communication Engineering, Korea University of Technology and Education(KOREATECH))
Lee, Jong-sung (Department of Electrical, Electronics & Communication Engineering, Korea University of Technology and Education(KOREATECH))
Oh, Chang-heon (Department of Electrical, Electronics & Communication Engineering, Korea University of Technology and Education(KOREATECH))
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.1, 2020 , pp. 8-14 More about this Journal
Abstract
The human impact of particulate matter are revealed and demand for improved forecast accuracy is increasing. Recently, efforts is made to improve the accuracy of PM10 predictions by using machine learning, but prediction performance is decreasing due to the particulate matter data with a large rate of low concentration occurrence. In this paper, separation prediction model by concentration is proposed to improve the accuracy of PM10 particulate matter forecast. The low and high concentration prediction model was designed using the weather and air pollution factors in Cheonan, and the performance comparison with the prediction models was performed. As a result of experiments with RMSE, MAPE, correlation coefficient, and AQI accuracy, it was confirmed that the predictive performance was improved, and that 20.62% of the AQI high-concentration prediction performance was improved.
Keywords
Particulate matters; Deep Learning; Artificial neural network; Deep neural network;
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Times Cited By KSCI : 4  (Citation Analysis)
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