Forecasting Air Pollution using a Modified Compositional Learning Approach

Major air pollutants, especially fine particles PM2.5, are generally associated with adverse health effects, including cardiac and respiratory morbidity. The aim of this paper is to find the best combination of machine learning techniques to forecast the Air Quality Index (AQI) using the Beijing air quality datasets. The dataset consists [among other] of six air pollutant attributes - PM2.5, PM10, SO
2
, NO
2
, CO and O
3
that are considered important factors in calculating the Air Quality Index. Our initial results showed that Linear Regression model is not adequate in predicting and forecasting the air pollutants. Random Forest and Random Committee models performed better in terms of MAE and RMSE values compared to Linear Regression. Furthermore, it was noticed that Random Forest performs better in terms of accuracy for certain features but not all while Random Committee performs better in other set of features. This shows that using a "single" machine learning approach to predict or forecast the entire features set may not give the best accuracy. So, as a result, a modified compositional learning model with disentanglement using optimized hyperparameters and search space was designed. The results of this novel network show a marked improvement (3.34% to 78%) in terms of MAE and RMSE values when compared to Random Forest and Random Committee.