An Online Method Based on Random Forest for Air Pollutant Concentration Forecasting

With rapid modernization, air quality is becoming gradually deteriorate. To avoid the adverse effects of severe air pollution on human health, we need accurate real-time air quality prediction. The factors relevant to air pollutant concentration forecasting contain simultaneously numeric type (temperature etc.) and non-numeric type (wind direction etc.). Random Forest has many advantages, which includes it can deal with numeric features and non-numeric features. In this study, an online forecasting method based on Random Forest is proposed to predict the concentrations of three kinds of air pollutants (PM
_2.5
, NO
₂
, SO
₂
), 24 hours in advance. The sliding window is used to take the recent data to retrain Random Forest prediction model and the well-trained models is used to predict the dependent variable at target moment. Before prediction model is trained, a variable selection method based on Random Forests (VSURF) is used to select the factors that are relevant to the forecast of air pollutant concentrations. We evaluate our method with dataset from Microsoft Research. Comparison with baseline methods shows that our method achieve state-of-art performance on air pollutant concentration forecasting. Experimental results also indicate that the features we selected using VSURF method are most important predictors for the prediction of three kinds of air pollutant concentrations.