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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.

Ensemble based J48 and random forest based C6H6 air pollution detection