Air prediction analysis based on accuracy for air quality index using modified random forest novel technique in comparison with logistic regression.

A prototype simulator to forecast the pollutants level indicator of air quality index using Modified Random Forest Technique (MRFNT) in comparison with Logistic Regression (LR). Materials and Methods: The proposed model MRFNT has the ability to deal with nonlinear data and helps to process redundant data to predict accuracy, involves bootstrap and bagging technique to process the dataset. The CPCB dataset was collected from National Air Quality of the Indian government to experiment this research. Results: Based on the research performed on the air pollutant dataset by using MRFNT and LR, the mean accuracy achieved 99.96% and 96.36% respectively. The statistical analysis was performed with 20 samples by considering G power alpha to 0.8, significance value to p<0.05, Confidence Interval (CI) of 95%, the result shows the existence of significance with p<0.001Based on the result and statistical proofs, MRFNT forecast the air pollutants level accurately and significantly better than LR. [ABSTRACT FROM AUTHOR]

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