Comparative Analysis of PM10 Prediction Performance between Neural Network Models

• Published in: Journal of information and communication convergence engineering Vol. 19; no. 4; pp. 241 - 247
• Main Authors: Jung, Yong-Jin, Oh, Chang-Heon
• Format: Journal Article
• Language: Korean
• Published: 2021
• Subjects: Deep neural network; Recurrent neural network; Particulate matter; Long short-term memory; Neural network
• ISSN: 2234-8255; 2234-8883

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.