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Ensemble Deep Learning Based Air Pollution Prediction for Sustainable Smart Cities

Computer Systems Science & Engineering 2024年第3期48卷 627-643页

作者： Maha Farouk Sabir Mahmoud Ragab Adil o.khadidos Khaled H.Alyoubi alaa o.khadidos Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia 3Department of MathematicsFaculty of ScienceAl-Azhar UniversityNaser CityCairo11884Egypt Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz UniversityJeddahSaudi Arabia

Big data and information and communication technologies can be important to the effectiveness of smart *** on the maximal attention on smart city sustainability,developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability ***-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make *** to air pollution occurs a main environmental problem in smart city *** effect of the deep learning(DL)approach quickly increased and penetrated almost every domain,comprising air pollution ***,this article develops a new Coot optimization Algorithm with an Ensemble Deep Learning based Air Pollution Prediction(CoAEDL-APP)system for Sustainable Smart *** projected CoAEDL-APP algorithm accurately forecasts the presence of air quality in the sustainable smart city *** achieve this,the CoAEDL-APP technique initially performs a linear scaling normalization(LSN)approach to pre-process the input *** 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 *** 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%.

关键词： Sustainability smart cities air pollution prediction ensemble learning coot optimization algorithm

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