文章基本信息

标题：Multiple Linear Regression (MLR) and Principal Component Regression (PCR) for Ozone (O ₃) Concentrations Prediction
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作者：Nur Nazmi Liyana Mohd Napi ; Mohammad Syazwan Noor Mohamed ; Samsuri Abdullah 等
期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2020
卷号：616
期号：1
DOI：10.1088/1755-1315/616/1/012004
语种：English
出版社：IOP Publishing
摘要：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.