Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{183062,
        author = {Shraddha Ishwarchandra Ghonsikar and Pravin R. Rathod},
        title = {Air Quality Index Prediction Using Ensemble Model},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {761-764},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183062},
        abstract = {In smart cities, air pollution has harmful impacts on human physical health and the quality of living environment. correctly predicting air quality is important for developing effective strategies to reduce air pollution and promote healthier, more sustainable environments. Tracking and predicting air pollution is essential for enabling individuals to make well informed choices that safeguard their health. Predicting air quality is vital for public health, environmental management, and the development of effective policies. This research focuses on predicting the Air Quality Index (AQI) using machine learning techniques, with an emphasis on improving model efficiency and prediction accuracy. This study presents a comparative analysis of machine learning algorithms for predictive modeling, focusing on an ensemble model combining Deep Learning + XGBoost + SHAP Feature Importance and the algorithms from the referred base paper such as Decision Tree Regression and Random Forest Regression. The performance of each algorithm is evaluated using three key metrics: the coefficient of determination (R² Score), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). These metrics provide insights into the accuracy, consistency, and reliability of the models’ predictions. Among the evaluated approaches, the Deep Learning + XGBoost + SHAP ensemble model demonstrates superior overall performance, offering the most accurate and robust predictions across all evaluation criteria.},
        keywords = {Prediction, Machine Learning, Air Quality Index, Coefficient of Determination (R² Score), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), XGBoost (eXtreme Gradient Boosting), SHAP (SHapley Additive exPlanations).},
        month = {August},
        }