Prediction of column ozone concentrations using multiple regression analysis and principal component analysis techniques: A case study in peninsular Malaysia

作者： | Kok Chooi Tan, Hwee San Lim, Mohd Zubir Mat Jafri |

作者单位： |
^{1}School 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： | Ozone; Multiple regression analysis; Principal component analysis; Peninsular Malaysia; Correlation 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.... |

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