Estimation of missing values in environmental data set using interpolation technique: fitting on lognormal distribution

M.N. Norazian, . and A. Mohd Mustafa Al Bakri, . and Y. Ahmad Shukri, . and R. Nor Azam, . and Z. Lufti, . (2008) Estimation of missing values in environmental data set using interpolation technique: fitting on lognormal distribution. [Proceedings Paper]

The problem of imputation of missing data emerges in many areas especially environment field. These data usually contained missing values due to many factors such as machine failures changes in the siting monitors routine maintenance and human error. Incomplete data set usually cause bias due to differences between observed and unobserved data. One approach that commonly used for handling missing data is imputation technique. This paper discusses three interpolation methods that are linear quadratic and cubic. A total of 8567 observations of particulate matter PM10 data for a year were used to compare between the three methods when fitting the lognormal distribution. The goodness-of-fit were obtained using three performance indicators that are mean absolute error MAE root mean squared error RMSE and coefficient of determination R2. It was found that linear interpolation method give the best fit with smallest error value for MAE is 1.99 and highest R2 that is 0.9889.

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Abstract

The problem of imputation of missing data emerges in many areas especially environment field. These data usually contained missing values due to many factors such as machine failures changes in the siting monitors routine maintenance and human error. Incomplete data set usually cause bias due to differences between observed and unobserved data. One approach that commonly used for handling missing data is imputation technique. This paper discusses three interpolation methods that are linear quadratic and cubic. A total of 8567 observations of particulate matter PM10 data for a year were used to compare between the three methods when fitting the lognormal distribution. The goodness-of-fit were obtained using three performance indicators that are mean absolute error MAE root mean squared error RMSE and coefficient of determination R2. It was found that linear interpolation method give the best fit with smallest error value for MAE is 1.99 and highest R2 that is 0.9889.

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Item Type:	Proceedings Paper
Additional Information:	Available at Perpustakaan Sultan Abdul Samad Universiti Putra Malaysia 43400 UPM Serdang Selangor Malaysia. GE90 M3I61 2008 Call Number
AGROVOC Term:	Environment
AGROVOC Term:	Quadratic programming
AGROVOC Term:	Mathematics
AGROVOC Term:	Distribution
Geographical Term:	MALAYSIA
Depositing User:	Ms. Suzila Mohamad Kasim
Last Modified:	24 Apr 2025 05:14
URI:	http://webagris.upm.edu.my/id/eprint/11804

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