Probability distributions comparative analysis in assessing rainfall process in time and space

The need for a reliable rainfall model to produce accurate simulation of rainfall series is imperative in water resources planning. Simulated series are used when there are shortages of observed series at location of interest. This study focuses on modelling of rainfall series with a range of pro...

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Main Authors:	Abas, Norzaida, M. R., Siti Musliha, M. D., Zalina, Abdul Halim, Syafrina
Format:	Article
Language:	English English
Published:	IAEME Publication 2017
Subjects:	GB Physical geography TD Environmental technology. Sanitary engineering
Online Access:	http://irep.iium.edu.my/63000/ http://irep.iium.edu.my/63000/ http://irep.iium.edu.my/63000/1/63000_Probability%20distributions%20comparative%20analysis_article.pdf http://irep.iium.edu.my/63000/2/63000_Probability%20distributions%20comparative%20analysis_scopus.pdf

id	iium-63000
recordtype	eprints
spelling	iium-630002018-04-09T00:47:50Z http://irep.iium.edu.my/63000/ Probability distributions comparative analysis in assessing rainfall process in time and space Abas, Norzaida M. R., Siti Musliha M. D., Zalina Abdul Halim, Syafrina GB Physical geography TD Environmental technology. Sanitary engineering The need for a reliable rainfall model to produce accurate simulation of rainfall series is imperative in water resources planning. Simulated series are used when there are shortages of observed series at location of interest. This study focuses on modelling of rainfall series with a range of probability distributions representing rainfall intensity of the Space-Time Neyman Scott (ST-NS) model. Theoretically, the ST-NS model is constructed by having parameters to represent the physical attributes of rainfall process. Therefore having appropriate distributions to describe the parameters are critical so that credible rainfall series could be generated. In this study, the performance of four probability distributions namely Mixed-Exponential, Gamma, Weibull and Generalized Pareto in representing rainfall intensity are assessed and compared. Model construction of the ST-NS model involved the merging of rainfall data from sixteen stations located all over Peninsular Malaysia. Simulations of hourly rainfall series for each distribution are carried at out of sample site. Performance assessments between the distributions are conducted using Root Mean Square Error, Akaike Information Criterion, Bayesian Information Criterion, Kolmogrov-Smirnov Test and Anderson-Darling Test. Results revealed that mixture type distributions tend to perform better. The performance of both Mixed-Exponential and Generalized Pareto are very similar and both are equally good at representing rain intensity in Peninsular Malaysia. The adopted method and the results could also be extended to other tropical regions. IAEME Publication 2017-10 Article PeerReviewed application/pdf en http://irep.iium.edu.my/63000/1/63000_Probability%20distributions%20comparative%20analysis_article.pdf application/pdf en http://irep.iium.edu.my/63000/2/63000_Probability%20distributions%20comparative%20analysis_scopus.pdf Abas, Norzaida and M. R., Siti Musliha and M. D., Zalina and Abdul Halim, Syafrina (2017) Probability distributions comparative analysis in assessing rainfall process in time and space. International Journal of Civil Engineering and Technology, 8 (10). pp. 1679-1688. ISSN 0976-6308 E-ISSN 0976-6316 http://www.iaeme.com/MasterAdmin/uploadfolder/IJCIET_08_10_168/IJCIET_08_10_168.pdf
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	GB Physical geography TD Environmental technology. Sanitary engineering
spellingShingle	GB Physical geography TD Environmental technology. Sanitary engineering Abas, Norzaida M. R., Siti Musliha M. D., Zalina Abdul Halim, Syafrina Probability distributions comparative analysis in assessing rainfall process in time and space
description	The need for a reliable rainfall model to produce accurate simulation of rainfall series is imperative in water resources planning. Simulated series are used when there are shortages of observed series at location of interest. This study focuses on modelling of rainfall series with a range of probability distributions representing rainfall intensity of the Space-Time Neyman Scott (ST-NS) model. Theoretically, the ST-NS model is constructed by having parameters to represent the physical attributes of rainfall process. Therefore having appropriate distributions to describe the parameters are critical so that credible rainfall series could be generated. In this study, the performance of four probability distributions namely Mixed-Exponential, Gamma, Weibull and Generalized Pareto in representing rainfall intensity are assessed and compared. Model construction of the ST-NS model involved the merging of rainfall data from sixteen stations located all over Peninsular Malaysia. Simulations of hourly rainfall series for each distribution are carried at out of sample site. Performance assessments between the distributions are conducted using Root Mean Square Error, Akaike Information Criterion, Bayesian Information Criterion, Kolmogrov-Smirnov Test and Anderson-Darling Test. Results revealed that mixture type distributions tend to perform better. The performance of both Mixed-Exponential and Generalized Pareto are very similar and both are equally good at representing rain intensity in Peninsular Malaysia. The adopted method and the results could also be extended to other tropical regions.
format	Article
author	Abas, Norzaida M. R., Siti Musliha M. D., Zalina Abdul Halim, Syafrina
author_facet	Abas, Norzaida M. R., Siti Musliha M. D., Zalina Abdul Halim, Syafrina
author_sort	Abas, Norzaida
title	Probability distributions comparative analysis in assessing rainfall process in time and space
title_short	Probability distributions comparative analysis in assessing rainfall process in time and space
title_full	Probability distributions comparative analysis in assessing rainfall process in time and space
title_fullStr	Probability distributions comparative analysis in assessing rainfall process in time and space
title_full_unstemmed	Probability distributions comparative analysis in assessing rainfall process in time and space
title_sort	probability distributions comparative analysis in assessing rainfall process in time and space
publisher	IAEME Publication
publishDate	2017
url	http://irep.iium.edu.my/63000/ http://irep.iium.edu.my/63000/ http://irep.iium.edu.my/63000/1/63000_Probability%20distributions%20comparative%20analysis_article.pdf http://irep.iium.edu.my/63000/2/63000_Probability%20distributions%20comparative%20analysis_scopus.pdf
first_indexed	2023-09-18T21:29:18Z
last_indexed	2023-09-18T21:29:18Z
_version_	1777412402921340928

Probability distributions comparative analysis in assessing rainfall process in time and space

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