IJESD 2023 Vol.14(2): 155-159
doi: 10.18178/ijesd.2023.14.2.1428
doi: 10.18178/ijesd.2023.14.2.1428
Enhancing Air Quality Prediction Accuracy Using Hybrid Deep Learning
Trang Pham Thi Quynh*, Tuyen Nguyen Viet, Hang Duong Thi, and Kha Hoang Manh
Manuscript received June 23, 2022; revised August 15, 2022; accepted September 7, 2022.
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.)
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.)
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).General Information
- ISSN: 2010-0264 (Print); 2972-3698 (Online)
- Abbreviated Title: Int. J. Environ. Sci. Dev.
- Frequency: Bimonthly
- DOI: 10.18178/IJESD
- Editor-in-Chief: Prof. Richard Haynes
- Managing Editor: Ms. Cherry L. Chen
- Indexing: Scopus (CiteScore 2022: 1.4), Google Scholar, CNKI, ProQuest, EBSCO, etc.
- E-mail: ijesd@ejournal.net
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