Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Comparative Analysis of PM10 Prediction Performance between Neural Network Models

  • 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))
  • Received : 2021.11.11
  • Accepted : 2021.12.13
  • Published : 2021.12.31
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Abstract

Particulate matter has emerged as a serious global problem, necessitating highly reliable information on the matter. Therefore, various algorithms have been used in studies to predict particulate matter. In this study, we compared the prediction performance of neural network models that have been actively studied for particulate matter prediction. Among the neural network algorithms, a deep neural network (DNN), a recurrent neural network, and long short-term memory were used to design the optimal prediction model using a hyper-parameter search. In the comparative analysis of the prediction performance of each model, the DNN model showed a lower root mean square error (RMSE) than the other algorithms in the performance comparison using the RMSE and the level of accuracy as metrics for evaluation. The stability of the recurrent neural network was slightly lower than that of the other algorithms, although the accuracy was higher.

Keywords

Acknowledgement

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2019R1I1A3A01059038).

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