Forecasting Air Quality Index (AQI) Using Machine Learning Techniques

Air pollution is still a major problem in cities where pollutants, such as ozone (O<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>) and sulfur dioxide (SO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>), can harm health and the environment. Thus, being able to forecast the Air Quality Index (AQI) can help make better-informed decisions and interventions. This study assesses traditional machine learning (ML), deep learning (DL), and hybrid models in AQI prediction using real-world data from New York City from 2014 to 2015. The research involved comparing various machine learning models such as Random Forest, XGBoost, and Support Vector Regression, as well as Long Short-Term Memory (LSTM) networks and hybrid models combining ML with DL techniques. Model performance was evaluated based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination ($\mathbf{R}^{2}$) values in multiple neighborhoods. The authors found that Random Forest and XGBoost performed better than stand-alone LSTM on both accuracy of forecasting and stability. A hybrid model with Random Forest and LSTM was also more robust than the stand-alone LSTM models in all neighborhoods.