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Decision Tree-Based Analytics for Reducing Air Pollution

Author

Listed:
  • Ajanta Das

    (Department of Computer Science and Engineering, University of Engineering and Management, Kolkata 700160, India)

  • Anindita Desarkar

    (Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Kolkata Campus, Kolkata 700107, India)

Abstract

Air pollution indicates contaminated air which arises due to the effect of physical, biological or chemical alteration to the air in the atmosphere applicable both for indoors and outdoors. This situation arises when poisonous gases, dust or smoke enter into the atmosphere and make the surroundings vulnerable for any living beings as well as difficult for them to survive. Large numbers of premature deaths happen across the globe if exposed to these pollutants on a long-term basis as major portion of the cities have the pollution level above the threshold determined by World Health Organization (WHO). So appropriate measures need to be taken on a priority basis to reduce air pollution as well as save our planet. This paper proposes a novel air pollution reduction approach which collects source pollution data. After extraction of source data, it uses various databases (DBs) and then different decisions or classes are created. The decision tree was created with the help of Iterative Dichotomiser 3 (ID3) algorithm to implement the rule base appropriately depending on the air pollution level and a bunch of rule sets were derived from the decision tree further.

Suggested Citation

  • Ajanta Das & Anindita Desarkar, 2018. "Decision Tree-Based Analytics for Reducing Air Pollution," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 1-20, June.
    • Handle: RePEc:wsi:jikmxx:v:17:y:2018:i:02:n:s0219649218500156
    DOI: 10.1142/S0219649218500156
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    References listed on IDEAS

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    1. Riza, Lala Septem & Bergmeir, Christoph & Herrera, Francisco & Benítez, José M., 2015. "frbs: Fuzzy Rule-Based Systems for Classification and Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 65(i06).
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