@Article{csse.2023.041551,
AUTHOR = {Maha Farouk Sabir, Mahmoud Ragab, Adil O. Khadidos, Khaled H. Alyoubi, Alaa O. Khadidos},
TITLE = {Ensemble Deep Learning Based Air Pollution Prediction for Sustainable Smart Cities},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {48},
YEAR = {2024},
NUMBER = {3},
PAGES = {627--643},
URL = {http://www.techscience.com/csse/v48n3/56523},
ISSN = {},
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%.},
DOI = {10.32604/csse.2023.041551}
}