Multiple Linear Regression (MLR) and Principal Component Regression (PCR) for ozone (O3) concentrations prediction
Air quality; Economics; Environmental technology; Forecasting; Linear regression; Nitrogen oxides; Ozone; Wind; Air quality levels; Correlation coefficient; Influencing parameters; Multicollinearity; Multiple linear regressions; Principal component regression; Principle component analysis; Principle...
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2023
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my.uniten.dspace-250602023-05-29T16:06:36Z Multiple Linear Regression (MLR) and Principal Component Regression (PCR) for ozone (O3) concentrations prediction Mohd Napi N.N.L. Noor Mohamed M.S. Abdullah S. Mansor A.A. Ahmed A.N. Ismail M. 57224902975 57194940680 56509029800 57211858557 57214837520 57210403363 Air quality; Economics; Environmental technology; Forecasting; Linear regression; Nitrogen oxides; Ozone; Wind; Air quality levels; Correlation coefficient; Influencing parameters; Multicollinearity; Multiple linear regressions; Principal component regression; Principle component analysis; Principle component regression; Predictive analytics Rapid economic growth has led to an increase in ozone (O3) concentration which significantly affecting human health and environment. The prediction of O3 is complicated due to the redundancy of influencing parameters which introduce the multicollinearity problem. The aim of this study is to assess the best prediction model for O3 concentration which is Multiple Linear Regression (MLR) and Principle Component Regression (PCR). Data from 2012 to 2014 were used including O3, nitrogen dioxide (NO2), nitrogen oxide (O2), temperature, relative humidity and wind speed on hourly basis. Principle Component Analysis (PCA) was used in order to reduce multicollinearity problem, prior to the implementation of MLR. The hybrid model of PCR was selected as best -fitted models as it had higher correlation coefficient, R2 values compared with MLR model. In conclusion, the information from best-fitted prediction model can be used by local authorities to plan the precaution measure in combating and preserve the better air quality level. � 2020 Institute of Physics Publishing. All rights reserved. Final 2023-05-29T08:06:36Z 2023-05-29T08:06:36Z 2020 Conference Paper 10.1088/1755-1315/616/1/012004 2-s2.0-85100068898 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100068898&doi=10.1088%2f1755-1315%2f616%2f1%2f012004&partnerID=40&md5=ed57f25c19b55bca5cb37068c7a5b69d https://irepository.uniten.edu.my/handle/123456789/25060 616 1 12004 All Open Access, Bronze IOP Publishing Ltd Scopus |
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| description |
Air quality; Economics; Environmental technology; Forecasting; Linear regression; Nitrogen oxides; Ozone; Wind; Air quality levels; Correlation coefficient; Influencing parameters; Multicollinearity; Multiple linear regressions; Principal component regression; Principle component analysis; Principle component regression; Predictive analytics |
| author2 |
57224902975 |
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57224902975 Mohd Napi N.N.L. Noor Mohamed M.S. Abdullah S. Mansor A.A. Ahmed A.N. Ismail M. |
| format |
Conference Paper |
| author |
Mohd Napi N.N.L. Noor Mohamed M.S. Abdullah S. Mansor A.A. Ahmed A.N. Ismail M. |
| spellingShingle |
Mohd Napi N.N.L. Noor Mohamed M.S. Abdullah S. Mansor A.A. Ahmed A.N. Ismail M. Multiple Linear Regression (MLR) and Principal Component Regression (PCR) for ozone (O3) concentrations prediction |
| author_sort |
Mohd Napi N.N.L. |
| title |
Multiple Linear Regression (MLR) and Principal Component Regression (PCR) for ozone (O3) concentrations prediction |
| title_short |
Multiple Linear Regression (MLR) and Principal Component Regression (PCR) for ozone (O3) concentrations prediction |
| title_full |
Multiple Linear Regression (MLR) and Principal Component Regression (PCR) for ozone (O3) concentrations prediction |
| title_fullStr |
Multiple Linear Regression (MLR) and Principal Component Regression (PCR) for ozone (O3) concentrations prediction |
| title_full_unstemmed |
Multiple Linear Regression (MLR) and Principal Component Regression (PCR) for ozone (O3) concentrations prediction |
| title_sort |
multiple linear regression (mlr) and principal component regression (pcr) for ozone (o3) concentrations prediction |
| publisher |
IOP Publishing Ltd |
| publishDate |
2023 |
| _version_ |
1806428171593056256 |
| score |
13.214268 |