Staff View: Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area

Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area

Meteorological conditions and other gaseous pollutants generally impacted the development of ozone (O3) in the atmosphere. The purpose of this study was to create the best O3 model for forecasting O3 concentrations in the industrial area and to determine the variables that affect O3 concentrations....

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Main Authors:	Napi N.N.L.M., Abdullah S., Mansor A.A., Ghazali N.A., Ahmed A.N., Dom N.C., Ismail M.
Other Authors:	57702029500
Format:	Article
Published:	Penerbit UTHM 2024
Subjects:	gaseous pollutant industrial meteorological multiple linear regression Ozone
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id	my.uniten.dspace-34702
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spelling	my.uniten.dspace-347022024-10-14T11:21:52Z Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area Napi N.N.L.M. Abdullah S. Mansor A.A. Ghazali N.A. Ahmed A.N. Dom N.C. Ismail M. 57702029500 56509029800 57211858557 26430938300 57214837520 57217286875 57210403363 gaseous pollutant industrial meteorological multiple linear regression Ozone Meteorological conditions and other gaseous pollutants generally impacted the development of ozone (O3) in the atmosphere. The purpose of this study was to create the best O3 model for forecasting O3 concentrations in the industrial area and to determine the variables that affect O3 concentrations. Five-year data of meteorological and gaseous pollutants were used to analyze and develop the prediction model. Based on three distinct techniques, three separate multiple linear regression (MLR) prediction models of O3 concentration were developed. MLR3 had the highest correlation coefficient of 0.792 during development as compared to models MLR1 and MLR2. MLR2 was deemed the best O3 prediction model, however, since it had the lowest error values of root mean square error (3.976) and mean absolute error (3.548) when compared to other models. The establishment of an O3 prediction model can offer local governments with early information that could help them reduce and manage air pollution emissions. � 2023 UTHM Publisher. All rights reserved. Final 2024-10-14T03:21:52Z 2024-10-14T03:21:52Z 2023 Article 10.30880/ijie.2023.15.01.010 2-s2.0-85152670270 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152670270&doi=10.30880%2fijie.2023.15.01.010&partnerID=40&md5=c8852861b94efa67c188866ac16469e3 https://irepository.uniten.edu.my/handle/123456789/34702 15 1 106 117 All Open Access Bronze Open Access Green Open Access Penerbit UTHM Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	gaseous pollutant industrial meteorological multiple linear regression Ozone
spellingShingle	gaseous pollutant industrial meteorological multiple linear regression Ozone Napi N.N.L.M. Abdullah S. Mansor A.A. Ghazali N.A. Ahmed A.N. Dom N.C. Ismail M. Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area
description	Meteorological conditions and other gaseous pollutants generally impacted the development of ozone (O3) in the atmosphere. The purpose of this study was to create the best O3 model for forecasting O3 concentrations in the industrial area and to determine the variables that affect O3 concentrations. Five-year data of meteorological and gaseous pollutants were used to analyze and develop the prediction model. Based on three distinct techniques, three separate multiple linear regression (MLR) prediction models of O3 concentration were developed. MLR3 had the highest correlation coefficient of 0.792 during development as compared to models MLR1 and MLR2. MLR2 was deemed the best O3 prediction model, however, since it had the lowest error values of root mean square error (3.976) and mean absolute error (3.548) when compared to other models. The establishment of an O3 prediction model can offer local governments with early information that could help them reduce and manage air pollution emissions. � 2023 UTHM Publisher. All rights reserved.
author2	57702029500
author_facet	57702029500 Napi N.N.L.M. Abdullah S. Mansor A.A. Ghazali N.A. Ahmed A.N. Dom N.C. Ismail M.
format	Article
author	Napi N.N.L.M. Abdullah S. Mansor A.A. Ghazali N.A. Ahmed A.N. Dom N.C. Ismail M.
author_sort	Napi N.N.L.M.
title	Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area
title_short	Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area
title_full	Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area
title_fullStr	Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area
title_full_unstemmed	Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area
title_sort	different approaches of multiple linear regression (mlr) model in predicting ozone (o3) concentration in industrial area
publisher	Penerbit UTHM
publishDate	2024
_version_	1814061191725580288
score	13.222552

Different Approaches of Multiple Linear Regression (MLR) Model in Predicting Ozone (O3) Concentration in Industrial Area

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