Ozone concentration forecasting utilizing leveraging of regression machine learnings: A case study at Klang Valley, Malaysia

• Main Authors: Latif S.D., Lai V., Hahzaman F.H., Ahmed A.N., Huang Y.F., Birima A.H., El-Shafie A.
• 其他作者: 57216081524
• 格式: Article
• 出版: Elsevier B.V. 2025
• 主题: Air pollution; Errors; Landforms; Learning systems; Mean square error; Ozone; Regression analysis; Support vector machines; Case-studies; Gaussian process regression; Ground-level ozone; Klang valley; Machine-learning; Malaysia; Ozone concentration; Ozone concentration forecasting; Regression machine learning; Regression modelling; Forecasting

At Klang Valley, ground-level ozone is a significant source of air pollution. Ozone (O3) concentration is affected by meteorological conditions and air pollutants. Linear Regression Models (LRM), Regression Trees (RT), Support Vector Machines (SVM), Ensembles of Trees (ET), Gaussian Process Regressi...