Abstract: Predicting haze is crucial in controlling air pollution to reduce its impact, especially on human health. Accurate prediction of extreme values is vital to raising public awareness of this issue and better understanding of air quality management. Extreme values in air pollution refer to unusually high measure-ments of pollutants that diverge significantly from the normal range of observed values. Extreme values are normally caused by haze from various factors. Neglecting extreme values can cause unreasonable predictions. Therefore, this study aims to evaluate the performance of a tree-based algorithm in predicting haze events. Predictive analytics were based on hourly air pollution data from 2013 to 2022 in Shah Alam, Malaysia. The ten parameters are chosen Relative Humidity (RH), Temperature (T), Wind Direction (WD), Wind Speed (WS), PM10, NOx, NO2, SO2, O3 and CO. Decision Tree (DT), Gradient Boosting Regression (GBR) and Extreme Gradient Boosting (XGBoost) are compared in determining the best approach for modeling PM10 concentrations for the next 24 hours (PM10,t+24h) for overall air quality data and three air quality blocks: Good air quality (Block 1), Moderate air quality (Block 2) and Extreme air quality (Block 3). The performance of RMSE, MAE and MAPE indicate that XGBoost outperforms GBR and DT with the RMSE(21.5921), MAE(14.2396) and MAPE(0.4816). When evaluating the performance across the three air quality blocks, XGBoost remains as the top-performing model. However, XGBoost faces challenges in accurately predicting extreme values.

Mahiran Muhammad, Ahmad Zia Ul-Saufie and Fadhilah Ahmad Radi, “Evaluating the Performance of Tree-Based Model in Predicting Haze Events in Malaysia” International Journal of Advanced Computer Science and Applications(IJACSA), 16(4), 2025. http://dx.doi.org/10.14569/IJACSA.2025.01604107

@article{Muhammad2025,
title = {Evaluating the Performance of Tree-Based Model in Predicting Haze Events in Malaysia},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.01604107},
url = {http://dx.doi.org/10.14569/IJACSA.2025.01604107},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {4},
author = {Mahiran Muhammad and Ahmad Zia Ul-Saufie and Fadhilah Ahmad Radi}
}