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A Hybrid Deep Learning Approach for PM2.5 Concentration Prediction in Smart Environmental Monitoring

Minh Thanh Vo1, Anh H. Vo2, Huong Bui3, Tuong Le4,5,*

1 Faculty of Information Technology, HUTECH University, Ho Chi Minh City, Vietnam
2 Natural Language Processing and Knowledge Discovery Laboratory, Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
3 Faculty of Computing Fundamentals, FPT University, Ho Chi Minh City, Vietnam
4 Laboratory for Artificial Intelligence, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City, Vietnam
5 Faculty of Information Technology, Van Lang University, Ho Chi Minh City, Vietnam

* Corresponding Author: Tuong Le. Email: email

Intelligent Automation & Soft Computing 2023, 36(3), 3029-3041. https://doi.org/10.32604/iasc.2023.034636

Abstract

Nowadays, air pollution is a big environmental problem in developing countries. In this problem, particulate matter 2.5 (PM2.5) in the air is an air pollutant. When its concentration in the air is high in developing countries like Vietnam, it will harm everyone’s health. Accurate prediction of PM2.5 concentrations can help to make the correct decision in protecting the health of the citizen. This study develops a hybrid deep learning approach named PM25-CBL model for PM2.5 concentration prediction in Ho Chi Minh City, Vietnam. Firstly, this study analyzes the effects of variables on PM2.5 concentrations in Air Quality HCMC dataset. Only variables that affect the results will be selected for PM2.5 concentration prediction. Secondly, an efficient PM25-CBL model that integrates a convolutional neural network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) is developed. This model consists of three following modules: CNN, Bi-LSTM, and Fully connected modules. Finally, this study conducts the experiment to compare the performance of our approach and several state-of-the-art deep learning models for time series prediction such as LSTM, Bi-LSTM, the combination of CNN and LSTM (CNN-LSTM), and ARIMA. The empirical results confirm that PM25-CBL model outperforms other methods for Air Quality HCMC dataset in terms of several metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE).

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APA Style
Vo, M.T., Vo, A.H., Bui, H., Le, T. (2023). A hybrid deep learning approach for PM2.5 concentration prediction in smart environmental monitoring. Intelligent Automation & Soft Computing, 36(3), 3029-3041. https://doi.org/10.32604/iasc.2023.034636
Vancouver Style
Vo MT, Vo AH, Bui H, Le T. A hybrid deep learning approach for PM2.5 concentration prediction in smart environmental monitoring. Intell Automat Soft Comput . 2023;36(3):3029-3041 https://doi.org/10.32604/iasc.2023.034636
IEEE Style
M.T. Vo, A.H. Vo, H. Bui, and T. Le "A Hybrid Deep Learning Approach for PM2.5 Concentration Prediction in Smart Environmental Monitoring," Intell. Automat. Soft Comput. , vol. 36, no. 3, pp. 3029-3041. 2023. https://doi.org/10.32604/iasc.2023.034636



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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