PREDICTION OF DAYTIME AND NIGHTTIME GROUND-LEVEL OZONE USING THE HYBRID REGRESSION MODELS

Ozone is one of the major challenges for the air quality community due to its adverse impact on the environment and human health. This study seeks to improve the understanding of underlying mechanisms for several developed models for ozone prediction. We aim to establish a robust prediction model fo...

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Main Authors: Ahmad A.N., Abdullah S., Mansor A.A., Dom N.C., Ahmed A.N., Ismail N.A., Ismail M.
Other Authors: 57810266500
Format: Article
Published: Asian Research Publishing Network 2024
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spelling my.uniten.dspace-344972024-10-14T11:20:11Z PREDICTION OF DAYTIME AND NIGHTTIME GROUND-LEVEL OZONE USING THE HYBRID REGRESSION MODELS Ahmad A.N. Abdullah S. Mansor A.A. Dom N.C. Ahmed A.N. Ismail N.A. Ismail M. 57810266500 56509029800 57211858557 57217286875 57214837520 57014388900 57210403363 cluster multiple linear regression Prediction principal component analysis Ozone is one of the major challenges for the air quality community due to its adverse impact on the environment and human health. This study seeks to improve the understanding of underlying mechanisms for several developed models for ozone prediction. We aim to establish a robust prediction model for ozone concentration up to the next four hours. Three years dataset including ozone (O3), nitrogen oxide (NOx), nitric oxide (NO), sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), particulate matter (PM10, PM2.5), wind speed, solar radiation, temperature, and relative humidity (RH) were used in this study. The data were analyzed by using Multiple Linear Regression (MLR), Principal Component Regression (PCR), and Cluster-Multiple Linear Regression (CMLR) in predicting the next hours of O3 concentration. Results show that the MLR models executed high accuracy for O3t+1 (R2= 0.313), O3,t+2 (R2= 0.265), O3,t+3 (R2= 0.227) and O3,t+4 (R2= 0.217) as the best fitted-model. In conclusion, the MLR model is suitable for the next hour's O3 concentration prediction. � 2006-2023 Asian Research Publishing Network (ARPN). All rights reserved. Final 2024-10-14T03:20:11Z 2024-10-14T03:20:11Z 2023 Article 10.59018/0623162 2-s2.0-85170659879 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170659879&doi=10.59018%2f0623162&partnerID=40&md5=026d21d5a776b182860c8efe57fc8411 https://irepository.uniten.edu.my/handle/123456789/34497 18 11 1258 1269 Asian Research Publishing Network 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 cluster
multiple linear regression
Prediction
principal component analysis
spellingShingle cluster
multiple linear regression
Prediction
principal component analysis
Ahmad A.N.
Abdullah S.
Mansor A.A.
Dom N.C.
Ahmed A.N.
Ismail N.A.
Ismail M.
PREDICTION OF DAYTIME AND NIGHTTIME GROUND-LEVEL OZONE USING THE HYBRID REGRESSION MODELS
description Ozone is one of the major challenges for the air quality community due to its adverse impact on the environment and human health. This study seeks to improve the understanding of underlying mechanisms for several developed models for ozone prediction. We aim to establish a robust prediction model for ozone concentration up to the next four hours. Three years dataset including ozone (O3), nitrogen oxide (NOx), nitric oxide (NO), sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), particulate matter (PM10, PM2.5), wind speed, solar radiation, temperature, and relative humidity (RH) were used in this study. The data were analyzed by using Multiple Linear Regression (MLR), Principal Component Regression (PCR), and Cluster-Multiple Linear Regression (CMLR) in predicting the next hours of O3 concentration. Results show that the MLR models executed high accuracy for O3t+1 (R2= 0.313), O3,t+2 (R2= 0.265), O3,t+3 (R2= 0.227) and O3,t+4 (R2= 0.217) as the best fitted-model. In conclusion, the MLR model is suitable for the next hour's O3 concentration prediction. � 2006-2023 Asian Research Publishing Network (ARPN). All rights reserved.
author2 57810266500
author_facet 57810266500
Ahmad A.N.
Abdullah S.
Mansor A.A.
Dom N.C.
Ahmed A.N.
Ismail N.A.
Ismail M.
format Article
author Ahmad A.N.
Abdullah S.
Mansor A.A.
Dom N.C.
Ahmed A.N.
Ismail N.A.
Ismail M.
author_sort Ahmad A.N.
title PREDICTION OF DAYTIME AND NIGHTTIME GROUND-LEVEL OZONE USING THE HYBRID REGRESSION MODELS
title_short PREDICTION OF DAYTIME AND NIGHTTIME GROUND-LEVEL OZONE USING THE HYBRID REGRESSION MODELS
title_full PREDICTION OF DAYTIME AND NIGHTTIME GROUND-LEVEL OZONE USING THE HYBRID REGRESSION MODELS
title_fullStr PREDICTION OF DAYTIME AND NIGHTTIME GROUND-LEVEL OZONE USING THE HYBRID REGRESSION MODELS
title_full_unstemmed PREDICTION OF DAYTIME AND NIGHTTIME GROUND-LEVEL OZONE USING THE HYBRID REGRESSION MODELS
title_sort prediction of daytime and nighttime ground-level ozone using the hybrid regression models
publisher Asian Research Publishing Network
publishDate 2024
_version_ 1814061123236790272
score 13.209306