Forecasting of Kelantan’s air pollution index [AP] PM2.5 using support vector machine [SVM] / Nur Fithrinnisaa Zamani

Forecasting the air pollution index has become a popular topic in recent years due to the impact of air pollution on environmental and human health. The Support Vector Machine (SVM) is not only appropriate for object classification, regression analysis and pattern recognition, it can also be used in...

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Main Author:	Zamani, Nur Fithrinnisaa
Format:	Thesis
Language:	English
Published:	2021
Subjects:	Multivariate analysis Cluster analysis Longitudinal method Time-series analysis Air pollution and its control
Online Access:	https://ir.uitm.edu.my/id/eprint/44440/1/44440.pdf
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id	my-uitm-ir.44440
record_format	uketd_dc
spelling	my-uitm-ir.444402021-04-22T07:22:39Z Forecasting of Kelantan’s air pollution index [AP] PM2.5 using support vector machine [SVM] / Nur Fithrinnisaa Zamani 2021-03-30 Zamani, Nur Fithrinnisaa Multivariate analysis. Cluster analysis. Longitudinal method Time-series analysis Air pollution and its control Forecasting the air pollution index has become a popular topic in recent years due to the impact of air pollution on environmental and human health. The Support Vector Machine (SVM) is not only appropriate for object classification, regression analysis and pattern recognition, it can also be used in time series forecasting. The work presented here aims to compare the accuracy of different types of the kernel function in Support Vector Machine (SVM) and to build a forecasting system for Kelantan’s air pollution index for PM2.5 using Support Vector Machine (SVM). The data used was provided by the Department of Environment (DOE) and was recorded from two Continuous Air Quality Monitoring Stations (CAQM) located at Tanah Merah and Kota Bharu. The results of the model were analyzed by using mean absolute error (MAE) and root mean squared error (RMSE). It is found that the proposed model using Radial Basis Function (RBF) kernel function with its parameters of cost and gamma equal to 100 can effectively and accurately forecast the air pollution index 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. 2021-03 Thesis https://ir.uitm.edu.my/id/eprint/44440/ https://ir.uitm.edu.my/id/eprint/44440/1/44440.pdf text en public degree Universiti Teknologi Mara Perlis Faculty of Computer & Mathematical Sciences
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Multivariate analysis Cluster analysis Longitudinal method Time-series analysis Air pollution and its control
spellingShingle	Multivariate analysis Cluster analysis Longitudinal method Time-series analysis Air pollution and its control Zamani, Nur Fithrinnisaa Forecasting of Kelantan’s air pollution index [AP] PM2.5 using support vector machine [SVM] / Nur Fithrinnisaa Zamani
description	Forecasting the air pollution index has become a popular topic in recent years due to the impact of air pollution on environmental and human health. The Support Vector Machine (SVM) is not only appropriate for object classification, regression analysis and pattern recognition, it can also be used in time series forecasting. The work presented here aims to compare the accuracy of different types of the kernel function in Support Vector Machine (SVM) and to build a forecasting system for Kelantan’s air pollution index for PM2.5 using Support Vector Machine (SVM). The data used was provided by the Department of Environment (DOE) and was recorded from two Continuous Air Quality Monitoring Stations (CAQM) located at Tanah Merah and Kota Bharu. The results of the model were analyzed by using mean absolute error (MAE) and root mean squared error (RMSE). It is found that the proposed model using Radial Basis Function (RBF) kernel function with its parameters of cost and gamma equal to 100 can effectively and accurately forecast the air pollution index 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.
format	Thesis
qualification_level	Bachelor degree
author	Zamani, Nur Fithrinnisaa
author_facet	Zamani, Nur Fithrinnisaa
author_sort	Zamani, Nur Fithrinnisaa
title	Forecasting of Kelantan’s air pollution index [AP] PM2.5 using support vector machine [SVM] / Nur Fithrinnisaa Zamani
title_short	Forecasting of Kelantan’s air pollution index [AP] PM2.5 using support vector machine [SVM] / Nur Fithrinnisaa Zamani
title_full	Forecasting of Kelantan’s air pollution index [AP] PM2.5 using support vector machine [SVM] / Nur Fithrinnisaa Zamani
title_fullStr	Forecasting of Kelantan’s air pollution index [AP] PM2.5 using support vector machine [SVM] / Nur Fithrinnisaa Zamani
title_full_unstemmed	Forecasting of Kelantan’s air pollution index [AP] PM2.5 using support vector machine [SVM] / Nur Fithrinnisaa Zamani
title_sort	forecasting of kelantan’s air pollution index [ap] pm2.5 using support vector machine [svm] / nur fithrinnisaa zamani
granting_institution	Universiti Teknologi Mara Perlis
granting_department	Faculty of Computer & Mathematical Sciences
publishDate	2021
url	https://ir.uitm.edu.my/id/eprint/44440/1/44440.pdf
_version_	1783734718631510016

Forecasting of Kelantan’s air pollution index [AP] PM2.5 using support vector machine [SVM] / Nur Fithrinnisaa Zamani

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