A new model of air quality prediction using lightweight machine learning

Air pollution has become one of the environmental concerns in recent years due to its harmful threats to human health. To inform people about the air quality in their living areas, it is essential to measure the extent of pollution in the atmosphere. Air pollution sensors are assembled at static, fixed-site measurement monitoring stations to acquire data. The data can be processed at the fixed stations or transmitted to the server to predict the Air Quality Index (AQI). Some previous studies applied machine learning algorithms to predict the AQI. Even though those works showed good performance on specific data, the results are not consistent on different datasets. Moreover, to serve the need for low-cost AQI tracking and prediction, lightweight machine learning algorithms can be directly integrated into microcontroller hardware systems. This study proposed a new method that combines (i) air pollution data processing techniques and (ii) lightweight machine learning algorithms to enhance the AQI predicting performance. Three algorithms, namely Decision Tree, Random Forest, and XGBoost, were compared via three evaluation metrics: MAE, RMSE, and R-2 to propose the best model in AQI prediction. Two different public datasets, which were both collected in different regions in India were used to verify our proposed method. XGBoost outperformed in predicting the AQI values. Thus, XGBoost is selected for the low-cost AQI prediction device assembled at fixed-site measurement stations.