Ensemble based J48 and random forest based C6H6 air pollution detection

Gagandeep Kaur¹ , Harmanpreet Kaur²

1. Computer Science & Engineering, Sri Sai college of Engineering and Technology, Manawala, Amritsar, India.
2. Computer Science & Engineering, Sri Sai college of Engineering and Technology, Manawala, Amritsar, India.

Section:Research Paper, Product Type: Isroset-Journal
Vol.6 , Issue.2 , pp.41-50, Apr-2018

CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.4150

Online published on Apr 30, 2018

Copyright © Gagandeep Kaur, Harmanpreet Kaur . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Air pollution has become a critical challenge for today’s world. An efficient monitoring of air pollution gases can help to reduce the pollution in the air. Air pollution cause us many diseases such as cancer etc. Benzene (C6H6) turn out to be more challenging issue in our society, because its sensors are costly to deploy and also not feasible to add too many sensors in urban areas. Therefore, in this paper an efficient monitoring of C6H6 gas has been done by using the ensemble approach. It is feasible to estimate C6H6 by using machine learning because there exists relationship between gases. Extensive experiments have been carried out to evaluate the effectiveness of the proposed technique. It has been found that the proposed technique significantly improves the performance of existing machine learning techniques.

Key-Words / Index Term :
C6H6 • Air pollution • Random forest • Machine learning

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