ArticleScientific reports2026
Regression based hybrid machine learning model performance evaluation on air quality index prediction in Kolkata.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Air pollution, especially elevated particulate matter concentrations, presents a substantial risk to public health and environmental sustainability in urban regions. By employing machine learning and hybrid ensemble models, this study develops a robust frame work for predicting the Air Quality Index (AQI). A multi-step imputation method was used to preprocess the dataset containing metrological variables and air contaminants in order to handle missing values. AQI was selected as the target variable. To assess the possibility of target dependency, two feature configurations were taken in to consideration: a full-featured set and a reduced set that excluded PM2.5 and PM10. Multiple models were used, including Linear Regression, Decision Tree, Random Forest, Gradient Boosting, KNN, MLP and LSTM as well as ensemble methods like Voting and Stacking regressor. Baseline models, namely persistence and SMA were incorporated for comparative analysis. To assess the performance RMSE, MAE, MAPE, RMSLE and R
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