In this study, the Support Vector Regression (SVR) model was used to forecast air quality time series data which is the particulate matter 10 micrometers or less in diameter (PM10) in Malaysia. Several SVR kernel functions, which are the Linear, Polynomial and Radial Basis Function (RBF) kernels, were considered in this study to determine the most suitable kernel function for forecasting the PM10 time series. The period of the data is from 5th July 2017 to 31st January 2019 consists of five air quality monitoring stations which are Kangar station in Perlis, Tasek Ipoh station in Perak, Shah Alam station in Selangor, Pasir Gudang station in Johor and Kuala Terengganu station in Terengganu. Model performance was compared based on the testing dataset's mean squared error (MSE) values. The results show that the SVR model with Radial Basis Function kernel is more suitable for forecasting the PM10 time series compared to the Linear and Polynomial kernels.
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29 August 2023
6TH INTERNATIONAL CONFERENCE ON MATHEMATICAL APPLICATIONS IN ENGINEERING
9–10 August 2022
Kuala Lumpur, Malaysia
Research Article|
August 29 2023
Comparison of support vector regression (SVR) kernel functions for predicting PM10 time series data in Malaysia
Mohd Aftar Abu Bakar;
Mohd Aftar Abu Bakar
a)
1
Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia
, 43600 UKM Bangi, Selangor, Malaysia
a)Corresponding author: [email protected]
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Noratiqah Mohd Ariff;
Noratiqah Mohd Ariff
b)
1
Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia
, 43600 UKM Bangi, Selangor, Malaysia
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Mohd Shahrul Mohd Nadzir;
Mohd Shahrul Mohd Nadzir
c)
2
Department of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaan Malaysia
, 43600 UKM Bangi, Selangor, Malaysia
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Lau Zhi Ying
Lau Zhi Ying
d)
1
Department of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia
, 43600 UKM Bangi, Selangor, Malaysia
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a)Corresponding author: [email protected]
AIP Conf. Proc. 2880, 050003 (2023)
Citation
Mohd Aftar Abu Bakar, Noratiqah Mohd Ariff, Mohd Shahrul Mohd Nadzir, Lau Zhi Ying; Comparison of support vector regression (SVR) kernel functions for predicting PM10 time series data in Malaysia. AIP Conf. Proc. 29 August 2023; 2880 (1): 050003. https://doi.org/10.1063/5.0165674
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