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  • Open Access

    ARTICLE

    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

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 627-643, 2024, DOI:10.32604/csse.2023.041551 - 20 May 2024

    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… More >

  • Open Access

    ARTICLE

    Air Pollution Prediction Via Graph Attention Network and Gated Recurrent Unit

    Shun Wang1, Lin Qiao2, Wei Fang3, Guodong Jing4, Victor S. Sheng5, Yong Zhang1,*

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 673-687, 2022, DOI:10.32604/cmc.2022.028411 - 18 May 2022

    Abstract PM2.5 concentration prediction is of great significance to environmental protection and human health. Achieving accurate prediction of PM2.5 concentration has become an important research task. However, PM2.5 pollutants can spread in the earth’s atmosphere, causing mutual influence between different cities. To effectively capture the air pollution relationship between cities, this paper proposes a novel spatiotemporal model combining graph attention neural network (GAT) and gated recurrent unit (GRU), named GAT-GRU for PM2.5 concentration prediction. Specifically, GAT is used to learn the spatial dependence of PM2.5 concentration data in different cities, and GRU is to extract the… More >

  • Open Access

    ARTICLE

    Air Pollution Prediction Using Dual Graph Convolution LSTM Technique

    R. Saravana Ram1, K. Venkatachalam2, Mehedi Masud3, Mohamed Abouhawwash4,5,*

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1639-1652, 2022, DOI:10.32604/iasc.2022.023962 - 24 March 2022

    Abstract In current scenario, Wireless Sensor Networks (WSNs) has been applied on variety of applications such as targets tracking, natural resources investigation, monitoring on unapproachable place and so on. Through the sensor nodes, the information for the applications is gathered and transferred. The physical coordination of these sensor nodes is determined, and it is called as localization. The WSN localization methods are studied widely for recent research with the study of small proportion of the sensor node called anchor nodes and their positions are determined through the GPS devices. Sometimes sensor nodes can be a IoT… More >

  • Open Access

    ARTICLE

    Gaussian Support Vector Machine Algorithm Based Air Pollution Prediction

    K. S. Bhuvaneshwari1, J. Uma2, K. Venkatachalam3, Mehedi Masud4, Mohamed Abouhawwash5,6,*, T. Logeswaran7

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 683-695, 2022, DOI:10.32604/cmc.2022.021477 - 03 November 2021

    Abstract Air pollution is one of the major concerns considering detriments to human health. This type of pollution leads to several health problems for humans, such as asthma, heart issues, skin diseases, bronchitis, lung cancer, and throat and eye infections. Air pollution also poses serious issues to the planet. Pollution from the vehicle industry is the cause of greenhouse effect and CO2 emissions. Thus, real-time monitoring of air pollution in these areas will help local authorities to analyze the current situation of the city and take necessary actions. The monitoring process has become efficient and dynamic… More >

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