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http://dx.doi.org/10.12673/jant.2021.25.5.409

Comparative Study of Performance of Deep Learning Algorithms in Particulate Matter Concentration Prediction  

Cho, Kyoung-Woo (AI Testing Team, Telecommunications Technology Association)
Jung, Yong-jin (Department of Electrical, Electronics and Communication Engineering, Korea University of Technology and Education(KOREATECH))
Oh, Chang-Heon (Department of Electrical, Electronics and Communication Engineering, Korea University of Technology and Education(KOREATECH))
Publication Information
Journal of Advanced Navigation Technology / v.25, no.5, 2021 , pp. 409-414 More about this Journal
Abstract
The growing concerns on the emission of particulate matter has prompted a demand for highly reliable particulate matter forecasting. Currently, several studies on particulate matter prediction use various deep learning algorithms. In this study, we compared the predictive performances of typical neural networks used for particulate matter prediction. We used deep neural network(DNN), recurrent neural network, and long short-term memory algorithms to design an optimal predictive model on the basis of a hyperparameter search. The results of a comparative analysis of the predictive performances of the models indicate that the variation trend of the actual and predicted values generally showed a good performance. In the analysis based on the root mean square error and accuracy, the DNN-based prediction model showed a higher reliability for prediction errors compared with the other prediction models.
Keywords
Neural network; Deep neural network; Recurrent neural network; Long short-term memory; Particulate matter;
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