Multimodal imputation-based stacked ensemble for prediction and classification of air quality index in Indian cities

Abstract

Nowadays, monitoring and predicting the air quality is very much needed to identify and control the adverse health effects due to the low air quality, especially in developing countries like India. Recently, it has been an interesting research topic to predict the air quality index (AQI) values and levels using machine learning algorithms. In this paper, we proposed a multimodal imputation based stacked ensemble (MISE) model to classify and predict the quality of air. The multimodel imputation is constructed using various imputation techniques such as KNN Impute, MICE and SVD Impute. We experimented the proposed model with various tree based algorithms such as Random Forest, XGBoost and Extra Tree to identify the best classification and regression model for the dataset. The Stacked ensemble is developed using above algorithms for classifying the AQI bucket. Based on the experimentational study, it is observed that stacked ensemble performed better in classifying AQI with an accuracy of 96.45% using SMOTE method. The proposed stacking model also performed better than other classifier with an accuracy of 91.13% on the imbalanced data. The proposed method MISE is also applied on the dataset for identifying the AQI score using tree based regression algorithms. The stacked ensemble performed better with an R2 score of 0.9687, MAE of 0.1052 and MSE of 0.0272 compared to existing models.