Predicting air quality using random forest: A case study in Amman-Zarqa
Farah Alzu’bi, Abdulla Al-Rawabdeh, Ali Almagbile
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 ( NO 2 ) and determine the factors which that most impact CO and NO 2 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 NO 2 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 NO 2 , with BU index showing a slightly higher percentage in NO 2 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 NO 2 models ranges from 3.78 to 7.30, and RMSE values range from 4.93 to 9.23.