International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Prediction of Air Quality Index Using Time Series Modelling: A Review Study

Author(s) Vanshika Saini, Richa
Country India
Abstract Air pollution is a serious environmental issue that affects public health and climate change. Accurate forecasting of the Air Quality Index (AQI) is essential to mitigate its adverse effects and implement effective pollution control measures. This review paper evaluates various time series modelling approaches used for AQI forecasting, including traditional statistical models such as ARIMA and SARIMA, hybrid models that combine statistical and machine learning techniques and deep learning-based approaches such as LSTM and fuzzy time series models. The findings suggest that while ARIMA and SARIMA are effective for short-term forecasting, hybrid models and deep learning techniques provide better accuracy by capturing complex temporal patterns. However, challenges such as data quality issues, computational cost, and regional variations affect the reliability of these models. Future research should focus on developing efficient hybrid approaches to integrate real-time data sources, enhance model interpretability, and improve AQI forecast accuracy. This study provides insights into the strengths and limitations of different forecasting techniques, providing a basis for future advancements in air quality forecasting.
Keywords Air, Air Pollution, Air Quality Index, Time Series, Analysis, Prediction, Forecasting, ARMA model, ARIMA model.
Field Mathematics > Statistics
Published In Volume 7, Issue 2, March-April 2025
Published On 2025-03-14
DOI https://doi.org/10.36948/ijfmr.2025.v07i02.38907
Short DOI https://doi.org/g8937s
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E-ISSN 2582-2160
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