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An Intelligent Air Quality Prediction System Using Neuro-Fuzzy Temporal Classifier with Spatial Constraints

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Computational Intelligence for Clinical Diagnosis

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

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Abstract

Air pollution is capable of affecting human beings seriously, even leading to death, by damaging important organs. The air pollution level is not unique in a country or even in a city due to the different circumstances. The world requires a strong air quality prediction system to predict air quality by analyzing the current trends of air quality using data collected from different cities in a country. For this purpose, a new neuro-fuzzy temporal and spatial constraint to be aware of air quality is proposed for predicting the air quality of a city or country in future. In this chapter, we propose a new classifier called the Neuro-Fuzzy Temporal Classification algorithm (NFTCA) with spatial constraints (NFTCA-S) for predicting the air quality of the city/country. Moreover, an effective feature optimization technique called Butterfly Optimization Algorithm (BOA) is also proposed for enhancing accuracy of air quality prediction. The PM2.5 dataset is used in this work to evaluate the proposed air quality prediction system. The dataset is collected from the UCI Machine Learning Repository and the NCPC; these websites maintain the air pollution status of various states. The dataset is used as input for the training and testing procedures as well as being split into training and testing datasets at a ratio of 80:20. Finally, the prediction system demonstrated its value by accurately predicting the concentrations of sulfur dioxide, carbon monoxide, and nitrogen oxides.

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Authors and Affiliations

  1. Research Scholar, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India

    S. Anu Priya

  2. Department of Information Technology, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India

    V. Khanaa

Authors
  1. S. Anu Priya
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  2. V. Khanaa
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Corresponding author

Correspondence to S. Anu Priya .

Editor information

Editors and Affiliations

  1. Thai-Nichi Institute of Technology, Bangkok, Thailand

    Ferdin Joe John Joseph

  2. Department of Automation and Applied Informatics, Aurel Vlaicu University of Arad, Arad, Arad, Romania

    Valentina Emilia Balas

  3. Department of Research, Arts and Humanities, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India

    S. Suman Rajest

  4. Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India

    R. Regin

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Anu Priya, S., Khanaa, V. (2023). An Intelligent Air Quality Prediction System Using Neuro-Fuzzy Temporal Classifier with Spatial Constraints. In: Joseph, F.J.J., Balas, V.E., Rajest, S.S., Regin, R. (eds) Computational Intelligence for Clinical Diagnosis. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-23683-9_11

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  • DOI: https://doi.org/10.1007/978-3-031-23683-9_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23682-2

  • Online ISBN: 978-3-031-23683-9

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