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Air Pollution Monitoring and Prediction Using Deep Learning

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Soft Computing for Security Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1428))

Abstract

Air pollution is one of the most significant threats to our environment; therefore, it is critical to investigate and monitor air pollution in current context. Moreover, air pollution remains as one of the major causes for environmental change, as well as human illness, and it should not be ignored; instead, it should be redefined by carefully examining its properties and recent modifications. The advanced prediction algorithms like machine learning and deep learning can be used to quantify the changes. Selective LSTM models are used to assess the PM2.5 concentrations present in the atmosphere in order to become connected with numerous estimation methods and discover the one that best accommodates policymakers and directors.

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Authors and Affiliations

  1. Delhi Technological University, Shahbad Daulatpur, Delhi, India

    Preet Singh,  Neeraj, Pawan Kumar & Manoj Kumar

Authors
  1. Preet Singh
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  2. Neeraj
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  3. Pawan Kumar
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  4. Manoj Kumar
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Corresponding author

Correspondence to Preet Singh .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Gnanamani College of Technology, Namakkal, Tamil Nadu, India

    G. Ranganathan

  2. Ryerson Communications Lab, Toronto, ON, Canada

    Xavier Fernando

  3. Department of Information Systems, University of Florida, Gainesville, FL, USA

    Selwyn Piramuthu

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Singh, P., Neeraj, Kumar, P., Kumar, M. (2023). Air Pollution Monitoring and Prediction Using Deep Learning. In: Ranganathan, G., Fernando, X., Piramuthu, S. (eds) Soft Computing for Security Applications. Advances in Intelligent Systems and Computing, vol 1428. Springer, Singapore. https://doi.org/10.1007/978-981-19-3590-9_53

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