Predicting air quality using random forest: A case study in Amman-Zarqa

Abstract

The Spatiotemporal variability of air quality is influenced by various factors over time. The objectives of this research are to create prediction models for Carbon monoxide (CO) and Nitrogen dioxide (NO2) and determine the factors which that most impact CO and NO2 monthly using Random Forest Prediction. The methodology relies on Random Forest Prediction to predict air quality monthly in 2021, incorporating eight variables land surface temperature (LST), normalized difference built-up index (NDBI), built-up index (BU index), normalized difference vegetation index (NDVI), digital elevation model (DEM), relative humidity (RH), wind speed (WS), and wind direction (WD). The results indicate that RH, elevation, WD, and LST are the most significant factors influencing CO concentrations, representing 33%, 24%, 12%, and 10% respectively at annual level in 2021. Similarly, WD, WS, RH, elevation and LST are the most importance factors impacting NO2 concentrations, representing 24%, 21%, 18%, 12%, and 10% respectively at an annual level in 2021. Furthermore, NDBI and BU index had the lowest impact in on both CO and NO2, with BU index showing a slightly higher percentage in NO2 models compared to CO models. Regarding cross-validation, the MAE values in CO models range from 0.11 to 0.18, and the RMSE values range from 0.14 to 0.23. Additionally, the MAE values in NO2 models ranges from 3.78 to 7.30, and RMSE values range from 4.93 to 9.23.

Keywords

• Machine learning
• Random forest prediction
• Air pollution
• Relative importance
• CO
• NO2