Estimation of Missing Rainfall Data Using Spatial Interpolation and Imputation Methods

This study is aimed to estimate missing rainfall data by dividing the analysis into three different percentages namely 5%, 10% and 20% in order to represent various cases of missing data. In practice, spatial interpolation methods are chosen at the first place to estimate missing data. These meth...

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Main Authors:	Roslinazairimah, Zakaria, Noor Fadhilah, Ahmad Radi, Muhammad Az-Zuhri, Azman
Format:	Conference or Workshop Item
Language:	English
Published:	2015
Subjects:	Q Science (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/6628/ http://umpir.ump.edu.my/id/eprint/6628/1/fist-2014-roslina-Estimation_of_Missing.pdf

id	ump-6628
recordtype	eprints
spelling	ump-66282015-03-31T08:40:17Z http://umpir.ump.edu.my/id/eprint/6628/ Estimation of Missing Rainfall Data Using Spatial Interpolation and Imputation Methods Roslinazairimah, Zakaria Noor Fadhilah, Ahmad Radi Muhammad Az-Zuhri, Azman Q Science (General) This study is aimed to estimate missing rainfall data by dividing the analysis into three different percentages namely 5%, 10% and 20% in order to represent various cases of missing data. In practice, spatial interpolation methods are chosen at the first place to estimate missing data. These methods include normal ratio (NR), arithmetic average (AA), coefficient of correlation (CC) and inverse distance (ID) weighting methods. The methods consider the distance between the target and the neighbouring stations as well as the correlations between them. Alternative method for solving missing data is an imputation method. Imputation is a process of replacing missing data with substituted values. A once-common method of imputation is single-imputation method, which allows parameter estimation. However, the single imputation method ignored the estimation of variability which leads to the underestimation of standard errors and confidence intervals. To overcome underestimation problem, multiple imputations method is used, where each missing value is estimated with a distribution of imputations that reflect the uncertainty about the missing data. In this study, comparison of spatial interpolation methods and multiple imputations method are presented to estimate missing rainfall data. The performance of the estimation methods used are assessed using the similarity index (S-index), mean absolute error (MAE) and coefficient of correlation (R). 2015 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/6628/1/fist-2014-roslina-Estimation_of_Missing.pdf Roslinazairimah, Zakaria and Noor Fadhilah, Ahmad Radi and Muhammad Az-Zuhri, Azman (2015) Estimation of Missing Rainfall Data Using Spatial Interpolation and Imputation Methods. In: AIP Conference Proceeding, 42, 1643 :The 2nd ISM International Statistical Conference (ISM-II 2014), 12-14 August 2014 , MS Garden Hotel, Kuantan. p. 42..
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	Q Science (General)
spellingShingle	Q Science (General) Roslinazairimah, Zakaria Noor Fadhilah, Ahmad Radi Muhammad Az-Zuhri, Azman Estimation of Missing Rainfall Data Using Spatial Interpolation and Imputation Methods
description	This study is aimed to estimate missing rainfall data by dividing the analysis into three different percentages namely 5%, 10% and 20% in order to represent various cases of missing data. In practice, spatial interpolation methods are chosen at the first place to estimate missing data. These methods include normal ratio (NR), arithmetic average (AA), coefficient of correlation (CC) and inverse distance (ID) weighting methods. The methods consider the distance between the target and the neighbouring stations as well as the correlations between them. Alternative method for solving missing data is an imputation method. Imputation is a process of replacing missing data with substituted values. A once-common method of imputation is single-imputation method, which allows parameter estimation. However, the single imputation method ignored the estimation of variability which leads to the underestimation of standard errors and confidence intervals. To overcome underestimation problem, multiple imputations method is used, where each missing value is estimated with a distribution of imputations that reflect the uncertainty about the missing data. In this study, comparison of spatial interpolation methods and multiple imputations method are presented to estimate missing rainfall data. The performance of the estimation methods used are assessed using the similarity index (S-index), mean absolute error (MAE) and coefficient of correlation (R).
format	Conference or Workshop Item
author	Roslinazairimah, Zakaria Noor Fadhilah, Ahmad Radi Muhammad Az-Zuhri, Azman
author_facet	Roslinazairimah, Zakaria Noor Fadhilah, Ahmad Radi Muhammad Az-Zuhri, Azman
author_sort	Roslinazairimah, Zakaria
title	Estimation of Missing Rainfall Data Using Spatial Interpolation and Imputation Methods
title_short	Estimation of Missing Rainfall Data Using Spatial Interpolation and Imputation Methods
title_full	Estimation of Missing Rainfall Data Using Spatial Interpolation and Imputation Methods
title_fullStr	Estimation of Missing Rainfall Data Using Spatial Interpolation and Imputation Methods
title_full_unstemmed	Estimation of Missing Rainfall Data Using Spatial Interpolation and Imputation Methods
title_sort	estimation of missing rainfall data using spatial interpolation and imputation methods
publishDate	2015
url	http://umpir.ump.edu.my/id/eprint/6628/ http://umpir.ump.edu.my/id/eprint/6628/1/fist-2014-roslina-Estimation_of_Missing.pdf
first_indexed	2023-09-18T22:02:34Z
last_indexed	2023-09-18T22:02:34Z
_version_	1777414496519716864

Estimation of Missing Rainfall Data Using Spatial Interpolation and Imputation Methods

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