Determination of the Best Single Imputation Algorithm for Missing Rainfall Data Treatment

The presence of missing rainfall data is inevitable due to error of recording, meteorological extremes and malfunction of instruments. Consequently, a competent imputation algorithm for missing data treatment algorithm is very much needed. There are several such efficient algorithms which have been...

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Main Authors:	Saeed, Gamil Abdulraqeb Abdullah, Chuan, Zun Liang, Roslinazairimah, Zakaria, Wan Nur Syahidah, Wan Yusoff
Format:	Article
Language:	English
Published:	Universiti Kebangsaan Malaysia 2016
Subjects:	QA Mathematics
Online Access:	http://umpir.ump.edu.my/id/eprint/17630/ http://umpir.ump.edu.my/id/eprint/17630/ http://umpir.ump.edu.my/id/eprint/17630/1/Determination%20of%20the%20Best%20Single%20Imputation%20Alogirthm%20for%20Missing%20Rainfall%20Data%20Treatment.pdf

id	ump-17630
recordtype	eprints
spelling	ump-176302018-10-17T03:16:22Z http://umpir.ump.edu.my/id/eprint/17630/ Determination of the Best Single Imputation Algorithm for Missing Rainfall Data Treatment Saeed, Gamil Abdulraqeb Abdullah Chuan, Zun Liang Roslinazairimah, Zakaria Wan Nur Syahidah, Wan Yusoff QA Mathematics The presence of missing rainfall data is inevitable due to error of recording, meteorological extremes and malfunction of instruments. Consequently, a competent imputation algorithm for missing data treatment algorithm is very much needed. There are several such efficient algorithms which have been introduced in earlier studies. However, the limitations of current algorithms are they are highly dependent on the information and homogeneity of adjoining rainfall stations. Therefore, this study is intended to introduce several single imputation algorithms for missing data treatment, which believed to be more competent in treating missing daily rainfall data without the need to depend on the information of adjoining rainfall stations. The proposed algorithms use descriptive measures of the data, including arithmetric means, geometric means, harmonic means, medians and midranges. These algorithms are tested on hourly rainfall data records from six selected rainfall stations located in the Kuantan River Basin. Based on the analysis, the proposed singular imputation algorithms, which treated missing data by geometric means, harmonic means and medians are more superior compared to the other imputation algorithms, irrespective of missing rates and rainfall stations. Universiti Kebangsaan Malaysia 2016 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/17630/1/Determination%20of%20the%20Best%20Single%20Imputation%20Alogirthm%20for%20Missing%20Rainfall%20Data%20Treatment.pdf Saeed, Gamil Abdulraqeb Abdullah and Chuan, Zun Liang and Roslinazairimah, Zakaria and Wan Nur Syahidah, Wan Yusoff (2016) Determination of the Best Single Imputation Algorithm for Missing Rainfall Data Treatment. Journal of Quality Measurement and Analysis (JQMA), 12 (1-2). pp. 79-87. ISSN 1823-5670 http://www.ukm.my/jqma/jqma12_1_2a.html
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Saeed, Gamil Abdulraqeb Abdullah Chuan, Zun Liang Roslinazairimah, Zakaria Wan Nur Syahidah, Wan Yusoff Determination of the Best Single Imputation Algorithm for Missing Rainfall Data Treatment
description	The presence of missing rainfall data is inevitable due to error of recording, meteorological extremes and malfunction of instruments. Consequently, a competent imputation algorithm for missing data treatment algorithm is very much needed. There are several such efficient algorithms which have been introduced in earlier studies. However, the limitations of current algorithms are they are highly dependent on the information and homogeneity of adjoining rainfall stations. Therefore, this study is intended to introduce several single imputation algorithms for missing data treatment, which believed to be more competent in treating missing daily rainfall data without the need to depend on the information of adjoining rainfall stations. The proposed algorithms use descriptive measures of the data, including arithmetric means, geometric means, harmonic means, medians and midranges. These algorithms are tested on hourly rainfall data records from six selected rainfall stations located in the Kuantan River Basin. Based on the analysis, the proposed singular imputation algorithms, which treated missing data by geometric means, harmonic means and medians are more superior compared to the other imputation algorithms, irrespective of missing rates and rainfall stations.
format	Article
author	Saeed, Gamil Abdulraqeb Abdullah Chuan, Zun Liang Roslinazairimah, Zakaria Wan Nur Syahidah, Wan Yusoff
author_facet	Saeed, Gamil Abdulraqeb Abdullah Chuan, Zun Liang Roslinazairimah, Zakaria Wan Nur Syahidah, Wan Yusoff
author_sort	Saeed, Gamil Abdulraqeb Abdullah
title	Determination of the Best Single Imputation Algorithm for Missing Rainfall Data Treatment
title_short	Determination of the Best Single Imputation Algorithm for Missing Rainfall Data Treatment
title_full	Determination of the Best Single Imputation Algorithm for Missing Rainfall Data Treatment
title_fullStr	Determination of the Best Single Imputation Algorithm for Missing Rainfall Data Treatment
title_full_unstemmed	Determination of the Best Single Imputation Algorithm for Missing Rainfall Data Treatment
title_sort	determination of the best single imputation algorithm for missing rainfall data treatment
publisher	Universiti Kebangsaan Malaysia
publishDate	2016
url	http://umpir.ump.edu.my/id/eprint/17630/ http://umpir.ump.edu.my/id/eprint/17630/ http://umpir.ump.edu.my/id/eprint/17630/1/Determination%20of%20the%20Best%20Single%20Imputation%20Alogirthm%20for%20Missing%20Rainfall%20Data%20Treatment.pdf
first_indexed	2023-09-18T22:24:28Z
last_indexed	2023-09-18T22:24:28Z
_version_	1777415873747746816

Determination of the Best Single Imputation Algorithm for Missing Rainfall Data Treatment

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