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Ensemble Deep Learning Based Air Pollution Prediction for Sustainable Smart Cities

Maha Farouk Sabir1, Mahmoud Ragab2,3,*, Adil O. Khadidos2, Khaled H. Alyoubi1, Alaa O. Khadidos1,4

1 Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
2 Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
3 Department of Mathematics, Faculty of Science, Al-Azhar University, Naser City, Cairo, 11884, Egypt
4 Center of Research Excellence in Artificial Intelligence and Data Science, King Abdulaziz University, Jeddah, Saudi Arabia

* Corresponding Author: Mahmoud Ragab. Email: email

Computer Systems Science and Engineering 2024, 48(3), 627-643. https://doi.org/10.32604/csse.2023.041551

Abstract

Big data and information and communication technologies can be important to the effectiveness of smart cities. Based on the maximal attention on smart city sustainability, developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability problems. Real-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make decisions. Relating to air pollution occurs a main environmental problem in smart city environments. The effect of the deep learning (DL) approach quickly increased and penetrated almost every domain, comprising air pollution forecast. Therefore, this article develops a new Coot Optimization Algorithm with an Ensemble Deep Learning based Air Pollution Prediction (COAEDL-APP) system for Sustainable Smart Cities. The projected COAEDL-APP algorithm accurately forecasts the presence of air quality in the sustainable smart city environment. To achieve this, the COAEDL-APP technique initially performs a linear scaling normalization (LSN) approach to pre-process the input data. For air quality prediction, an ensemble of three DL models has been involved, namely autoencoder (AE), long short-term memory (LSTM), and deep belief network (DBN). Furthermore, the COA-based hyperparameter tuning procedure can be designed to adjust the hyperparameter values of the DL models. The simulation outcome of the COAEDL-APP algorithm was tested on the air quality database, and the outcomes stated the improved performance of the COAEDL-APP algorithm over other existing systems with maximum accuracy of 98.34%.

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APA Style
Sabir, M.F., Ragab, M., Khadidos, A.O., Alyoubi, K.H., Khadidos, A.O. (2024). Ensemble deep learning based air pollution prediction for sustainable smart cities. Computer Systems Science and Engineering, 48(3), 627-643. https://doi.org/10.32604/csse.2023.041551
Vancouver Style
Sabir MF, Ragab M, Khadidos AO, Alyoubi KH, Khadidos AO. Ensemble deep learning based air pollution prediction for sustainable smart cities. Comput Syst Sci Eng. 2024;48(3):627-643 https://doi.org/10.32604/csse.2023.041551
IEEE Style
M.F. Sabir, M. Ragab, A.O. Khadidos, K.H. Alyoubi, and A.O. Khadidos "Ensemble Deep Learning Based Air Pollution Prediction for Sustainable Smart Cities," Comput. Syst. Sci. Eng., vol. 48, no. 3, pp. 627-643. 2024. https://doi.org/10.32604/csse.2023.041551



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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