Simulation study of adjusted spatial weighting method to estimate missing rainfall data

Missing value especially in environmental study is a common problem including in rainfall modelling. Incomplete data will alect the accuracy and elciency in any modelling process. In this study, simulation method is used to demonstrate the elciency of the old normal ratio inverse distance correlatio...

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Main Authors:	Muhammad Az-Zuhri, Azman, Roslinazairimah, Zakaria, Siti Zanariah, Satari
Format:	Conference or Workshop Item
Language:	English English
Published:	2018
Subjects:	QA Mathematics TD Environmental technology. Sanitary engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/24224/ http://umpir.ump.edu.my/id/eprint/24224/ http://umpir.ump.edu.my/id/eprint/24224/1/35.%20Simulation%20study%20of%20adjusted%20spatial%20weighting%20method.pdf http://umpir.ump.edu.my/id/eprint/24224/2/35.1%20Simulation%20study%20of%20adjusted%20spatial%20weighting%20method.pdf

id	ump-24224
recordtype	eprints
spelling	ump-242242019-05-21T03:36:42Z http://umpir.ump.edu.my/id/eprint/24224/ Simulation study of adjusted spatial weighting method to estimate missing rainfall data Muhammad Az-Zuhri, Azman Roslinazairimah, Zakaria Siti Zanariah, Satari QA Mathematics TD Environmental technology. Sanitary engineering Missing value especially in environmental study is a common problem including in rainfall modelling. Incomplete data will alect the accuracy and elciency in any modelling process. In this study, simulation method is used to demonstrate the elciency of the old normal ratio inverse distance correlation weighting method (ONRIDCWM) in solving missing rainfall data. The simulation study is used to identify the best parameters for correlation power of p, percentage of missing value and sample size, n of the ONRIDCWM by simulating for 10,000 times by varying the value of the parameters systematically. The results of the simulation are compared with other available weighting methods. The estimated complete rainfall data of the target station are compared and assessed with the observed data from the neighbouring station using mean, estimated bias (EB) and estimated root mean square error (ERMSE). The results show that ONRIDCWM is better than the other weighting methods for the correlation power of p at least four. For illustration of the weighting method, monthly rainfall data from Pahang has used to demonstrate the elciency of the method using three error indices: S-Index, mean absolute error (MAE) and correlation, R. 2018-11 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/24224/1/35.%20Simulation%20study%20of%20adjusted%20spatial%20weighting%20method.pdf pdf en http://umpir.ump.edu.my/id/eprint/24224/2/35.1%20Simulation%20study%20of%20adjusted%20spatial%20weighting%20method.pdf Muhammad Az-Zuhri, Azman and Roslinazairimah, Zakaria and Siti Zanariah, Satari (2018) Simulation study of adjusted spatial weighting method to estimate missing rainfall data. In: Simposium Kebangsaan Sains Matematik 2018 (SKSM2018), 28 - 29 November 2018 , Kota KInabalu sabah. pp. 1-8.. (Unpublished) http://www.ums.edu.my/fssa/index.php/en/sksm26
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	QA Mathematics TD Environmental technology. Sanitary engineering
spellingShingle	QA Mathematics TD Environmental technology. Sanitary engineering Muhammad Az-Zuhri, Azman Roslinazairimah, Zakaria Siti Zanariah, Satari Simulation study of adjusted spatial weighting method to estimate missing rainfall data
description	Missing value especially in environmental study is a common problem including in rainfall modelling. Incomplete data will alect the accuracy and elciency in any modelling process. In this study, simulation method is used to demonstrate the elciency of the old normal ratio inverse distance correlation weighting method (ONRIDCWM) in solving missing rainfall data. The simulation study is used to identify the best parameters for correlation power of p, percentage of missing value and sample size, n of the ONRIDCWM by simulating for 10,000 times by varying the value of the parameters systematically. The results of the simulation are compared with other available weighting methods. The estimated complete rainfall data of the target station are compared and assessed with the observed data from the neighbouring station using mean, estimated bias (EB) and estimated root mean square error (ERMSE). The results show that ONRIDCWM is better than the other weighting methods for the correlation power of p at least four. For illustration of the weighting method, monthly rainfall data from Pahang has used to demonstrate the elciency of the method using three error indices: S-Index, mean absolute error (MAE) and correlation, R.
format	Conference or Workshop Item
author	Muhammad Az-Zuhri, Azman Roslinazairimah, Zakaria Siti Zanariah, Satari
author_facet	Muhammad Az-Zuhri, Azman Roslinazairimah, Zakaria Siti Zanariah, Satari
author_sort	Muhammad Az-Zuhri, Azman
title	Simulation study of adjusted spatial weighting method to estimate missing rainfall data
title_short	Simulation study of adjusted spatial weighting method to estimate missing rainfall data
title_full	Simulation study of adjusted spatial weighting method to estimate missing rainfall data
title_fullStr	Simulation study of adjusted spatial weighting method to estimate missing rainfall data
title_full_unstemmed	Simulation study of adjusted spatial weighting method to estimate missing rainfall data
title_sort	simulation study of adjusted spatial weighting method to estimate missing rainfall data
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/24224/ http://umpir.ump.edu.my/id/eprint/24224/ http://umpir.ump.edu.my/id/eprint/24224/1/35.%20Simulation%20study%20of%20adjusted%20spatial%20weighting%20method.pdf http://umpir.ump.edu.my/id/eprint/24224/2/35.1%20Simulation%20study%20of%20adjusted%20spatial%20weighting%20method.pdf
first_indexed	2023-09-18T22:36:32Z
last_indexed	2023-09-18T22:36:32Z
_version_	1777416633053085696

Simulation study of adjusted spatial weighting method to estimate missing rainfall data

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