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http://dx.doi.org/10.6109/jkiice.2019.23.1.20

Conformity Assessment of Machine Learning Algorithm for Particulate Matter Prediction  

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))
Kang, Chul-gyu (Photo Team, SEMES Co., LTD.)
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.23, no.1, 2019 , pp. 20-26 More about this Journal
Abstract
Due to the human influence of particulate matter, various studies are being conducted to predict it using past data measured in the atmospheric environment monitoring network. However, it is difficult to precisely set the measurement environment and detailed conditions of the previously designed predictive model, and it is necessary to design a new predictive model based on the existing research results because of the problems such as the missing of the weather data. In this paper, as a previous study for particulate matter prediction, the conformity of the algorithm for particulate matter prediction was evaluated by designing the prediction model through the multiple linear regression and the artificial neural network, which are machine learning algorithms. As a result of the prediction performance comparison through RMSE, 18.13 for the MLR model and 14.31 for the MLP model, and the artificial neural network model was more conformable for predicting the particulate matter concentration.
Keywords
Particulate matter; Prediction; Machine learning; Deep learning; Artificial neural network;
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