Prediction, modelling, and forecasting of PM and AQI using hybrid machine learning

This paper proposes a combination of hybrid models like Input Variable Selection (IVS), Machine Learning (ML), and regression method to predict, model, and forecast the daily concentrations of particulate matter (PM1, PM2.5, PM10) and Air Quality Index (AQI). A sensor placed in the centre of Craiova, Romania, provides a two-year dataset for training, testing, and validation phases. The analysis identifies the most important predictor variables for PM prediction and forecasting. The coefficient of determination (R2) values in this stage exceeded 0.95 (95%), indicating a strong correlation between PM concentrations. The performance of the proposed models is evaluated by objective measures, including root mean squared error (RMSE) and standard deviation (a). RMSE ranged between 0.65 and 1 & mu;g/m3, while a has values between 2.75 and 4.1 & mu;g/m3, reflecting a high level of precision and a successful performance of the proposed models. Furthermore, 13 multivariable-based PM models are developed in this study and adjusted using a hybrid Least Square -Decision Tree approach. The R2 values for these adjusted models range from 0.66 to 0.75, while the RMSE and a vary between 8 and 9.1 & mu;g/m3. Finally, a handled application for multistep-ahead time series forecasting is elaborated by combining the Nonlinear System Identification (NARMAX) approach with Decision Tree machine learning. This application allows for forecasting PM concentrations and AQI for the next periods. The R2 values obtained in this stage surpass 0.93, indicating almost a high level of accuracy. The RMSE ranged between 4.43 and 6.25 & mu;g/m3, while the a ranged between 4.44 and 6.26 & mu;g/m3, further validating the precision of our forecasting model.

Automated summary

This paper proposes a combination of hybrid models to predict and forecast the daily concentrations of particulate matter (PM) and Air Quality Index (AQI) using Input Variable Selection (IVS), Machine Learning (ML), and regression method. The performance of the proposed models is evaluated and validated using objective measures, and high precision and successful performance are observed. Furthermore, multivariable-based PM models are developed and adjusted, and a handled application for multistep-ahead time series forecasting is elaborated, achieving a high level of accuracy in predicting PM concentrations and AQI for future periods.