Boosted Regression Tree (BRT) Model for PM10 Concentrations Prediction in Malaysia

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dc.contributor.authorNorazrin
dc.contributor.authorHazrul Abdul Hamid
dc.contributor.authorAhmad Shukri Yahaya
dc.coverage.publicationMalaysia
dc.date.accessioned2024-05-10T03:25:09Z
dc.date.available2024-05-10T03:25:09Z
dc.date.issued2023
dc.description.abstractThe 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).
dc.identifier.citationNorazrin, 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.
dc.identifier.urihttps://repoemc.ukm.my/handle/123456789/517
dc.language.isoen
dc.publisherIOP Publishing
dc.publisher.alternativeIOP Conference Series: Earth and Environmental Science
dc.titleBoosted Regression Tree (BRT) Model for PM10 Concentrations Prediction in Malaysia
dc.typeJournal

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