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

A prototype model to predict the pollutants level based on air quality index using Modified Random Forest Novel Technique (MRFNT) in comparison with Support Vector Regression (SVR). The proposed model, MRFNT uses the bootstrap and bagging technique on the nonlinear data, the pollutants level is pred...

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Bibliographic Details
Main Authors: Maheshwar, Thaginapelli, Sharma, K. Jai
Format: Conference Proceeding
Language:English
Subjects:
Air quality
Bagging
Confidence intervals
Data analysis
Data points
Error analysis
Outdoor air quality
Outliers (statistics)
Pollutants
Predictions
Regression models
Statistical analysis
Support vector machines
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Summary:A prototype model to predict the pollutants level based on air quality index using Modified Random Forest Novel Technique (MRFNT) in comparison with Support Vector Regression (SVR). The proposed model, MRFNT uses the bootstrap and bagging technique on the nonlinear data, the pollutants level is predicted accurately. To predict the air quality, the Air Quality Index CPCB dataset was collected from the National Air Quality Index aided by the Indian government. For better proficiency, bagging error and unbiased data points pre-processed to remove outliers and mix up with continuous categorical variables. The statistical analysis was performed using sample size 20 for each group to perform comparison. From the observed results, MRFNT has 99.96% and SVR has 98.36% accuracy and has better significance of 0.001 for the Confidence Interval (CI) 95% and Significance Value (p
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0227860