Implementing Decision Tree in Air Pollution Reduction Framework

  • Anindita DesarkarEmail author
  • Ajanta Das
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 77)


Air pollution, which is one of the biggest threats to the civilization, refers to the contaminated air. It happens due to occurrence of harmful gases, dust, and smoke into the atmosphere which is vulnerable to almost every living creature. It poses serious threats to environmental and social well-being. This paper proposes a layered air pollution reduction framework through implementing the decision tree approach. The proposed framework recommends suggestive measures for reducing air pollution level with the help of an innovative rule base along with mining proper data from the massive dataset. It also discusses the experimental results based on the decision tree approach which shows the implementation of the rule base depending on the pollution level by analyzing the impact factors like holiday, festival, political gathering, etc.


Predictive analysis Air pollution Knowledge discovery Decision tree Machine learning 


  1. 1.
    Warf, B.: Cities in the Telecommunications Age: The Fracturing of Geographies. Psychology Press, Hove (2000)Google Scholar
  2. 2.
    Jana, S., Rahman, Md.A., Pal, S.: Air pollution in Kolkata: an analysis of current status and interrelation between different factors. SEEU Rev. 8(1), 182–214 (2013). doi: Scholar
  3. 3.
    Shah, M.: Waiting for health care: a survey of a public hospital in Kolkata. Retrieved 2 Feb 2016, from
  4. 4.
    Bhaduri, S.: Vehicular growth and air quality at major traffic intersection points in Kolkata City, an efficient intervention strategies. SIJ Trans. Adv. Space Res. Earth Explor. 1(1), 19–25 (2013)Google Scholar
  5. 5.
  6. 6.
    Qiu, J., et al.: A survey of machine learning for big data processing. EURASIP J. Adv. Signal Process. 2016(1), 1–16 (2016)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Oberlin, S.: Machine Learning, Cognition, and Big Data. CA Technology Exchange (2012): 44Google Scholar
  8. 8.
    Kotsiantis, S.B., Zaharakis, I.D., Pintelas, P.E.: Machine learning: a review of classification and combining techniques. Artif. Intell. Rev. 26(3), 159–190 (2006)CrossRefGoogle Scholar
  9. 9.
    Pach, F.P., Abonyi, J.: Association rule and decision tree based methods for fuzzy rule base generation. Int. J. Comput. Electr. Autom. Control Inf. Eng. 2(1) (2008)Google Scholar
  10. 10.
    Julián, C.I.F., Ferri, C.: Airvlc.: an application for real-time forecasting urban air pollution. In: Proceedings of the 2nd International Workshop on Mining Urban Data, Lille, France (2015)Google Scholar
  11. 11.
    Air Quality Assessment. Retrieved 17 June 2016 from
  12. 12.
    Desarkar, A., Das, A.: A smart air pollution analytics framework. Presented in the International Conference on ICT for Sustainable Development (ICT4SD 2016), held in Bangkok, December 12–13, 2016 (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringBirla Institute of Technology Mesra, Deemed UniversityRanchiIndia

Personalised recommendations