Forecasting of Air Pollution Index PM2.5 Using Support Vector Machine(SVM)

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. Accessin...

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Formato:	article
Lenguaje:	EN
Publicado:	Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perlis 2020
Materias:	Probabilities. Mathematical statistics QA273-280 Technology T Technology (General) T1-995
Acceso en línea:	https://doaj.org/article/800326e34b4e4409a3c30fb269049d3f
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spelling	oai:doaj.org-article:800326e34b4e4409a3c30fb269049d3f2021-11-06T02:23:33ZForecasting of Air Pollution Index PM2.5 Using Support Vector Machine(SVM)2600-8793https://doaj.org/article/800326e34b4e4409a3c30fb269049d3f2020-10-01T00:00:00Zhttp://repeater.my/index.php/jcrinn/article/view/149https://doaj.org/toc/2600-8793 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 (API) accurately is very important to control its impact on environmental and human health. The work presented here aims to access air pollution index of PM2.5 using Support Vector Machine (SVM) and to compare the accuracy of four different types of the kernel function in Support Vector Machine (SVM). The data used 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 air pollution index with Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of 0.03868583 and 0.06251793 respectively for API in Kota Bharu and 0.03857308 (MAE) and 0.05895648 (RMSE) for API in Tanah Merah. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA PerlisarticleProbabilities. Mathematical statisticsQA273-280TechnologyTTechnology (General)T1-995ENJournal of Computing Research and Innovation, Vol 5, Iss 3 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Probabilities. Mathematical statistics QA273-280 Technology T Technology (General) T1-995
spellingShingle	Probabilities. Mathematical statistics QA273-280 Technology T Technology (General) T1-995 Forecasting of Air Pollution Index PM2.5 Using Support Vector Machine(SVM)
description	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 (API) accurately is very important to control its impact on environmental and human health. The work presented here aims to access air pollution index of PM2.5 using Support Vector Machine (SVM) and to compare the accuracy of four different types of the kernel function in Support Vector Machine (SVM). The data used 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 air pollution index with Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of 0.03868583 and 0.06251793 respectively for API in Kota Bharu and 0.03857308 (MAE) and 0.05895648 (RMSE) for API in Tanah Merah.
format	article
title	Forecasting of Air Pollution Index PM2.5 Using Support Vector Machine(SVM)
title_short	Forecasting of Air Pollution Index PM2.5 Using Support Vector Machine(SVM)
title_full	Forecasting of Air Pollution Index PM2.5 Using Support Vector Machine(SVM)
title_fullStr	Forecasting of Air Pollution Index PM2.5 Using Support Vector Machine(SVM)
title_full_unstemmed	Forecasting of Air Pollution Index PM2.5 Using Support Vector Machine(SVM)
title_sort	forecasting of air pollution index pm2.5 using support vector machine(svm)
publisher	Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perlis
publishDate	2020
url	https://doaj.org/article/800326e34b4e4409a3c30fb269049d3f
_version_	1718443990672474112

Forecasting of Air Pollution Index PM2.5 Using Support Vector Machine(SVM)

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