Ensemble Deep Learning Based Air Pollution Prediction for Sustainable Smart Cities  

作　　者：Maha Farouk Sabir Mahmoud Ragab Adil O.Khadidos Khaled H.Alyoubi Alaa O.Khadidos 

机构地区：[1]Information Systems Department,Faculty of Computing and Information Technology,King Abdulaziz University,Jeddah,21589,Saudi Arabia [2]Information Technology Department,Faculty [3]of Computing and Information Technology,King Abdulaziz University,Jeddah,21589,Saudi Arabia 3Department 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

出　　处：《Computer Systems Science & Engineering》2024年第3期627-643,共17页计算机系统科学与工程（英文）

基　　金：funded by the Deanship of Scientific Research(DSR),King Abdulaziz University(KAU),Jeddah,Saudi Arabia under Grant No.(IFPIP:631-612-1443).

摘　　要：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%.

关 键 词：SUSTAINABILITY smart cities air pollution prediction ensemble learning coot optimization algorithm 

分 类 号：X51[环境科学与工程—环境工程] TP18[自动化与计算机技术—控制理论与控制工程]