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Deep Learning Techniques for Air Pollution Prediction Using Remote Sensing Data

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Smart Technologies in Data Science and Communication

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 210))

Abstract

Due to Environmental and climate changes, these days, the whole world is facing air pollution and this problem is becoming further critical issues, which has terribly disturbed human lives as well as well-being. So, there is an immense need to search on air quality forecasting and has continuously considered as a key issue in environment safeguard. This paper covers the study associated with air pollution prediction using deep learning (DL) techniques based on remote sensing data. Major of the research work on air pollution are on the long-term forecasting of outdoor pollutants particulate matters (PM2.5 and PM10), ozone and nitrogen oxide. This paper discusses about the air pollution that causes the mortality and morbidity.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Andhra Pradesh, India

    Bhimavarapu Usharani & M. Sreedevi

Authors
  1. Bhimavarapu Usharani
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  2. M. Sreedevi
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Corresponding author

Correspondence to Bhimavarapu Usharani .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India

    Sanjoy Kumar Saha

  2. School of Science, Engineering and Information Technology, Federation University, Ballarat, VIC, Australia

    Paul S. Pang

  3. Department of Computer Science and Engineering, K L Deemed to be University, Vaddeswaram, Andhra Pradesh, India

    Debnath Bhattacharyya

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Usharani, B., Sreedevi, M. (2021). Deep Learning Techniques for Air Pollution Prediction Using Remote Sensing Data. In: Saha, S.K., Pang, P.S., Bhattacharyya, D. (eds) Smart Technologies in Data Science and Communication. Lecture Notes in Networks and Systems, vol 210. Springer, Singapore. https://doi.org/10.1007/978-981-16-1773-7_9

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