International Journal of Environmental Science and Development

Abstract—PM2.5 (Particulate Matter) and PM10 are the most common pollutants, and the increasing of concentration in the air will threaten people’s health. The machine learning method has recently been of particular interest to many researchers due to its effectiveness in air quality prediction models. Many solutions employing deep learning-based techniques such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and hybrid CNN-LSTM models to enhance air quality prediction accuracy have been developed. This paper proposes a hybrid Encoder STM model for predicting the next day to the next five days’ PM2.5 and PM10 concentrations in Hanoi. Additionally, we proposed five extended features to increase the accuracy of prediction. Then other models, namely the LSTM model and the Bidirectional LSTM model, are also considered for PM2.5 and PM10 concentration prediction. Our results show that the proposed approaches outperform other state-of-the-art deep learning-based methods on both Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) due to low error and the small number of features.

Index Terms—Urban air quality, PM2.5, PM10 prediction analysis, machine learning, hybrid deep learning

The authors are with Faculty of Electronics, Hanoi University of Industry, Hanoi, 100000, Vietnam. E-mail: nvtuyen@haui.edu.vn (T.N.V.), hangdt@haui.edu.vn (H.D.T.), khahoang@haui.edu.vn (K.H.M.)
*Correspondence: pham.trang@haui.edu.vn (T.P.T.Q.)

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Cite: Trang Pham Thi Quynh*, Tuyen Nguyen Viet, Hang Duong Thi, and Kha Hoang Manh, "Enhancing Air Quality Prediction Accuracy Using Hybrid Deep Learning," International Journal of Environmental Science and Development vol. 14, no. 2, pp. 155-159, 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).