Explainable forecasting of air quality index using a hybrid random forest and ARIMA model

Explainable forecasting of air quality index using a hybrid random forest and ARIMA model

Accurate and interpretable prediction of the Air Quality Index (AQI) is critical for public health decision-making and environmental policy enforcement. This study presents a hybrid forecasting framework that combines the strengths of Random Forest Regression (RFR) and Autoregressive Integrated Movi...

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Bibliographic Details
Main Authors: Anuradha Yenkikar, Ved Prakash Mishra, Manish Bali, Tabassum Ara
Format: Article
Language:English
Published: Elsevier 2025-12-01
Series:MethodsX
Subjects:
Air quality index (AQI)
Hybrid machine learning
Random forest regressor (RFR)
Autoregressive integrated moving average (ARIMA)
Explainable AI (XAI)
SHapley additive explanations (SHAP)
Online Access:http://www.sciencedirect.com/science/article/pii/S2215016125003619
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