Boosted Regression Tree (BRT) Model for PM10 Concentrations Prediction in Malaysia
Date
2023
Journal Title
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Volume Title
Publisher
IOP Publishing
Abstract
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).
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Citation
Norazrin, R., Hamid, H. A., & Yahaya, A. S. (2023). Boosted Regression Tree (BRT) model for PM10 concentrations prediction in Malaysia. In IOP Conference Series: Earth and Environmental Science (Vol. 1135, No. 1, p. 012041). IOP Publishing.