Forecasting air pollution index (API) PM2.5 using support vector machine (SVM) / Nor Hayati Shafii ... [et al.]

Shafii, Nor Hayati and Alias, Rohana and Zamani, Nur Fithrinnissaa and Fauzi, Nur Fatihah (2020) Forecasting air pollution index (API) PM2.5 using support vector machine (SVM) / Nor Hayati Shafii ... [et al.]. Journal of Computing Research and Innovation (JCRINN, 5 (3). pp. 43-53. ISSN 2600-8793

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Abstract

Air pollution is a current monitored problem in areas with high population density such as big cities. Many regions in Malaysia are facing extreme air quality issues. This situation is caused by several factors such as human behavior, environmental awareness and technological development. Accessing the air pollution index accurately is very important to control its impact on environmental and human health. The work presented here aims to access Air Pollution Index(API) of PM2.5accurately using Support Vector Machine (SVM) and to compare the accuracy of four different types of the kernel function in Support Vector Machine (SVM). SVM is relatively memory efficient and works relatively well in high dimensional spaces data which is better than the conventional method. The data used in this study is provided by the Department of Environment (DOE) and it is recorded from two Continuous Air Quality Monitoring Stations (CAQM) located at Tanah Merah and Kota Bharu. The results are analyzed using mean absolute error (MAE) and root mean squared error (RMSE). It is found that the proposed model using Radial Basis Function (RBF) with its parameters of cost and gamma equal to 100 can effectively and accurately forecast the API based on the model testing with 0.03868583 (MAE) and 0.06251793 (RMSE) for API in Kota Bharu and 0.03857308 (MAE) and 0.05895648 (RMSE) for API in Tanah Merah.

Metadata

Item Type: Article
Creators:
Creators
Email
Shafii, Nor Hayati
UNSPECIFIED
Alias, Rohana
UNSPECIFIED
Zamani, Nur Fithrinnissaa
UNSPECIFIED
Fauzi, Nur Fatihah
UNSPECIFIED
Subjects: Q Science > QA Mathematics > Multivariate analysis. Cluster analysis. Longitudinal method
Q Science > QA Mathematics > Time-series analysis
T Technology > TD Environmental technology. Sanitary engineering > Air pollution and its control
Divisions: Universiti Teknologi MARA, Perlis > Arau Campus > Faculty of Computer and Mathematical Sciences
Journal or Publication Title: Journal of Computing Research and Innovation (JCRINN
UiTM Journal Collections: UiTM Journal > Journal of Computing Research and Innovation (JCRINN)
ISSN: 2600-8793
Volume: 5
Number: 3
Page Range: pp. 43-53
Official URL: https://crinn.conferencehunter.com/
Item ID: 43378
Related URLs:
Uncontrolled Keywords: Forecasting air pollution index, API, Support Vector Machine (SVM), time series forecasting, kernel function, PM2.5
URI: https://ir.uitm.edu.my/id/eprint/43378

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43378

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