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

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.