Prediction of PM2.5 Dust Concentration Using Machine Learning Techniques To support forecasting, advanced Machine Learning technology is therefore employed to assist in the study of PM2.5 dust concentration prediction in advance.
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Abstract
Fine particulate matter (PM2.5) is a significant air pollution issue, particularly in urban areas with high emissions, such as Bangkok, Thailand, and Guangzhou, China—one of the most populous cities in the world. This research focuses on developing a predictive system for PM2.5 concentration using machine learning techniques, including Linear Regression (LR), Support Vector Regression (SVR), and XGBoost, to aid in air pollution monitoring and management. The dataset used in this study is a secondary source containing recorded PM2.5 values from Guangzhou, China, between 2010 and 2015. Experimental comparisons of the three models reveal that XGBoost demonstrates the highest predictive performance across all timeframes. Specifically, for the 1-hour ahead prediction, the XGBoost model incorporating historical PM2.5 averages and seasonal data achieved an R² of 0.6728, MAE of 12.06, and RMSE of 17.87, outperforming both LR and SVR. Furthermore, the predictive performance of all models declined as the forecasting timeframe increased, but XGBoost consistently outperformed the other methods in every scenario. The inclusion of seasonal information and historical PM2.5 averages significantly enhanced the model’s ability to predict future PM2.5 concentrations.
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