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http://dx.doi.org/10.22156/CS4SMB.2021.11.03.007

Comparison and analysis of prediction performance of fine particulate matter(PM2.5) based on deep learning algorithm  

Kim, Younghee (Department of Convergence Engineering, Graduate School of Venture, Hoseo University)
Chang, Kwanjong (Department of Convergence Engineering, Graduate School of Venture, Hoseo University)
Publication Information
Journal of Convergence for Information Technology / v.11, no.3, 2021 , pp. 7-13 More about this Journal
Abstract
This study develops an artificial intelligence prediction system for Fine particulate Matter(PM2.5) based on the deep learning algorithm GAN model. The experimental data are closely related to the changes in temperature, humidity, wind speed, and atmospheric pressure generated by the time series axis and the concentration of air pollutants such as SO2, CO, O3, NO2, and PM10. Due to the characteristics of the data, since the concentration at the current time is affected by the concentration at the previous time, a predictive model for recursive supervised learning was applied. For comparative analysis of the accuracy of the existing models, CNN and LSTM, the difference between observation value and prediction value was analyzed and visualized. As a result of performance analysis, it was confirmed that the proposed GAN improved to 15.8%, 10.9%, and 5.5% in the evaluation items RMSE, MAPE, and IOA compared to LSTM, respectively.
Keywords
Deep Learning; Fine particulate Matter(PM2.5); Convolutional Neural Network(CNN); Long Short-Term Memory(LSTM); Generative Adversarial Networks(GAN); Time Series Data;
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