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Influential of Outliers in Prediction of Ground-level Ozone Concentration
Norsahadah Binti Dor Baser
Supervisor: Dr. Nurul Adyani binti Ghazali
Bachelor of Technology (Environment)
School of Ocean Engineering
Universiti Malaysia Terengganu
Outliers are data which deviate too far from other observations. The contamination of
outliers in air pollution data always associated with bias results which presents less
accurate information of air pollution due to the non-reflecting conditions of the actual
phenomenon. This study aims to evaluate the influential of outliers on the prediction
of ozone (O3) concentration at selected sites in Klang Valley. The prediction models
were developed using Multiple Linear Regression (MLR) and evaluated by performance
indicator (PI); namely normalized absolute error (NAE) and index of agreement (IA).
Results show that non-outliers dataset represent better performance with smallest
error measures and highest accuracy measures. The PI for Shah Alam (NAE=-
0.222292 and IA=1.053347) and Petaling Jaya (NAE=0.054667 and IA=1.001445)
respectively. The model developed could be implemented among local authorities and
nongovernment organization (NGO) in order to prepare people with early action or
precaution.
62 | U M T U N D E R G R A D U A T E R E S E A R C H D A Y 2019