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Handling Missing Data in Air Pollution Datasets: A Comparison between
Linear Interpolation Method and Mean Top Bottom Method
Muhammad Hafizi Bin Mat Rahim
Supervisor: Dr. Nurul Adyani Bt Ghazali
Bachelor of Technology (Environment)
School of Ocean Engineering
Universiti Malaysia Terengganu
Missing data is one of common problems in real life datasets especially in air quality
datasets. The aim of this study is to apply missing values using Linear Interpolation
(LI) and Mean Top Bottom (MTB) techniques in air quality datasets at Kemaman and
Kuala Terengganu. Five performance indicators that are mean absolute error (MAE),
root mean square error (RMSE), coefficient of determination (R 2 ), prediction accuracy
(PA) and index of agreement (d2) were calculated in order to examine the best method
for replacing the missing values. From these performance indicators, mean top bottom
(MTB) method was found as the best method for predicting missing values compared
to others method. This was because MTB method gave the smallest values of MAE
and RMSE and the highest values for R 2 , PA and d2 in almost all parameters.
45 | 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