Spatial Air Quality Index and Air Pollutant Concentration prediction using Linear Regression based Recursive Feature Elimination with Random Forest Regression (RFERF): a case study in India.

In the last decade, air pollution has become one of the vital environmental issues and has expanded its wings day by day. Prediction of air quality plays a crucial role in warning people about the air quality levels. With the help of this, we can make the proper mechanism for reducing the overall impact of bad air quality on individuals' health. In this paper, we are focused on developing a mechanistic and quantitative prediction model for the prediction of the Air Quality Index (AQI) and Air Pollutant Concentration (NOx) levels with a clear environmental interpretation. The proposed model is based on the Linear Regression based Recursive Feature Elimination with Random Forest Regression (RFERF). For the experimental analysis, the seven well-established machine learning models have been taken, and these models are compared with our proposed model to find out their suitability and correctness. The Mean Absolute Percentage Error (MAPE), mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and Coefficient of Determination (R2 score) have been used to validate the performance of prediction models. For the prediction of AQI and NOx, the data of the Central Pollution Control Board of India has been taken. The proposed model performs superior as compared to other prediction models with better accuracy and a higher prediction rate. This work also explains that linking machine learning with sensor-generated AQI data for air quality prediction is an adequate and appropriate way to solve some related environment glitches. Apart from this, the impact of air pollution on individuals' health due to high levels of AQI, NOx, and other pollutants with the possible solutions has also been covered. [ABSTRACT FROM AUTHOR]