Publication: Ozone concentration forecasting utilizing leveraging of regression machine learnings: A case study at Klang Valley, Malaysia
Date
2024
Authors
Latif S.D.
Lai V.
Hahzaman F.H.
Ahmed A.N.
Huang Y.F.
Birima A.H.
El-Shafie A.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier B.V.
Abstract
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 Regression (GPR), and Neural Networks (NN) are utilized in a thorough analysis to determine the accuracy of various machine learning in forecasting the ground level O3 concentration. The primary associated contributions from this research are comparisons of regression statistical model performance based on indicators of root mean squared error (RMSE), coefficient of determination (R2), mean squared error (MSE), mean absolute error (MAE), prediction speed, and training time of regression models. Overall, exponential GPR outperformed other regression models in scenario 1 (S-1), scenario 2 (S-2), scenario (S-3), and scenario 4 (S-4) by incorporating multiple number of lags into respective scenarios and new method of testing ?re-substitution? performed more reliable and consistent than applying identical datasets to 20 % of model testing. The findings showed that GPR performed accurate results with R2 = 0.98, 0.95, 0.96, and 0.96 for S-1, S-2, S-3 and S-4 respectively. ? 2024 The Authors
Description
Keywords
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