Prediction of column ozone concentrations using multiple regression analysis and principal component analysis techniques: A case study in peninsular Malaysia
作者: Kok Chooi TanHwee San LimMohd Zubir Mat Jafri
作者单位: 1School of Physics, Universiti Sains Malaysia, 11800 Penang, Malaysia
刊名: Atmospheric Pollution Research, 2016, Vol.7 (3), pp.533-546
来源数据库: Elsevier Journal
DOI: 10.1016/j.apr.2016.01.002
关键词: OzoneMultiple regression analysisPrincipal component analysisPeninsular MalaysiaCorrelation coefficient
英文摘要: Abstract(#br)The aim of this study is to develop new algorithms of the column ozone (O 3 ) in Peninsular Malaysia using statistical methods. Four regression equations, denoted as O 3 NEM, O 3 SWM, (PCA1) O 3 NEM season, and (PCA2) O 3 SWM season, were developed. Multiple regression analysis (MRA) and principal component analysis (PCA) methods were utilized to achieve the objectives of the study. MRA was used to generate regression equations for O 3 NEM and O 3 SWM, whereas a combination of the MRA and PCA methods were used to generate regression equations for PCA1 and PCA2. The results of the best regression equations for the column O 3 through MRA by using four of the independent variables were highly correlated (R = 0.811 for SWM, R = 0.803 for NEM) for the six-year (2003–2008) data....
全文获取路径: Elsevier  (合作)
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