The aim of the study was to propose a Boosted Regression Tree (BRT) model for
predicting PM10 concentrations in the short term. Multiple Linear Regression
(MLR) and Boosted Regression Tree (BRT) models for short-term PM10
predictions are provided, and performance indicators (IA, R2, RMSE, MAE, and
MAPE) are used to find the appropriate model. The Department of Environment
Malaysia (DOE) provided seventeen years of daily average air quality monitoring
data, including eight parameters (PM10, wind speed, temperature, relative
humidity, NO2, SO2, CO, and O3) and five monitoring stations (Perai, Shah Alam,
Nilai, Larkin, and Pasir Gudang).

Categories: Kualiti, Udara
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