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International Journal of Environmental Sciences
Year : 2011, Volume : 2, Issue : 2
First page : ( 403) Last page : ( 410)
Online ISSN : 0976-4402.

Improving multiple linear regression model using principal component analysis for predicting PM10 concentration in Seberang Prai, Pulau Pinang

Ul-Saufie AZ1, Yahya AS*, Ramli N.A.

Clean Air Research Group, School of Civil Engineering, Universiti Sains Malaysia.

1Faculty of Computer and Mathematical Sciences, Universiti Teknologi Mara, Malaysia

*Email: ceazam@eng.usm.my

Online published on 11 December, 2012.


In recent decades, particulate matter is one of the prevalent pollutants recorded throughout Malaysia. The development of models to predict particulate matter less than and equal 10 micrometers (PM10) concentration is thus very useful because it can provide early warning to the population and for input into decision regarding abatement measures and air quality management. The aim of this study was to improve the predictive power of multiple linear regression models using principal components as input for predicting PM10 concentration for the next day. The developed model was compared with multiple linear regression models. Performance indicator such as Prediction Accuracy (PA), Coefficient of Determination (R2), Index of Agreement (IA), Normalised Absolute Error (NAE) and Root Mean Square Error (RMSE) were used to measure the accuracy of the models. Results showed that the use of principal component as inputs improved multiple linear regression models prediction by reducing their complexity and eliminating data collinearity.



Principal Component Analysis, Regression Models, PM10, Performance Indicator, Principal Component Regression.


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