Open Access

Deep Learning-based Environmental Air Quality Development using Remote Sensing based Stations

M. Mathan Kumar, hariharaiyyan@gmail.com
1Department of Computing Technologies, SRM University, KTR Campus, Chennai, TN, India S. Gayathri Priya, Department of Electronics and Communication Engineering, R.M.D. Engineering College, Chennai, TN, India S. Sabarunisha Begum, Department of Biotechnology, P.S.R. Engineering College, Sivakasi, TN, India Chandrasekar Perumal, Department of Electrical and Electronics Engineering, Vel Tech Rangarajan, Dr.Sagunthala R&D Institute of Science and Technology, TN, India P. Ramadevi, Department of Electronics and Communication Engineering, University College of Engineering, BIT Campus, Anna University, Tiruchirappalli, TN, India R. Sudha Department of Information Technology, PSNA College of Engineering and Technology, Dindigul, TN, India

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Abstract

Air pollution, comprised of harmful substances suspended in the air, tragically leads to millions of premature deaths on an annual basis. Although ground-based stations offer precise monitoring of air pollution, their effectiveness is confined to specific geographic areas. Conversely, satellite remote sensing technology holds the promise of broadening coverage, yet its primary focus remains on the upper layers of the atmosphere. In pursuit of a comprehensive solution, this study endeavors to revolutionize the representation of surface air quality on a global scale by harnessing the power of satellite data. Through the utilization of advanced techniques such as Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs), it amalgamates data streams from air pollution stations across the globe. Furthermore, socioeconomic and environmental data are seamlessly integrated with satellite images to construct sophisticated multiple models. The results of this innovative approach unveil the superiority of multiple models over their singular counterparts, boasting enhanced accuracy in air quality prediction. The efficient architecture of CNNs combined with the generative capabilities of GANs enables real-time or near-real-time monitoring of air pollution levels. This timely feedback is essential for implementing prompt interventions and mitigating the impact of air pollution on public health and the environment.

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Reference

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