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1860799414728130560
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| building |
INTELEK Repository
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Online Access
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| collectionurl |
https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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| date |
2016-10-26 10:07:06
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| eventvenue |
Sydney, Australia
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Restricted Document
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5913
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UniSZA
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0623-01-FH03-FIK-18-12080.pdf
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ITR
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oai_dc
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https://intelek.unisza.edu.my/intelek/pages/view.php?ref=5913
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| spelling |
5913 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=5913 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Conference Conference Paper application/pdf 4 1.6 Adobe Acrobat Pro DC 20 Paper Capture Plug-in ITR 2016-10-26 10:07:06 0623-01-FH03-FIK-18-12080.pdf UniSZA Private Access Determining probability Disyribution for Streamflow Regions using Partial L-Moments An attempt has been made to model the annual maximum streamflow, utilizing the guidelines in the regional flood frequency analysis. The Partial L-moments (PL-moments) at several censoring levels are employed to estimate the regional parameters of three extreme value distributions, namely; generalized extreme value (GEV), generalized logistic (GLO) and generalized Pareto (GPA) distributions. A total number of 18 streamflow stations located throughout the eastern region of Peninsular Malaysia were used as a case study. Firstly, the data is screening out for data verification and quality control. Next, identification of homogeneous regions is made using homogeneity test based on PL-moments. The PL - diagram is then constructed and GEV and GLO distributions appeared to be the acceptable distributions for representing the regional data. However, it is relatively difficult to identify a particular distribution that most fitted the regional data. Thus, goodness-of-fit test (Z-test) is used and the result showed that the most appropriate distribution for modeling maximum streamflow in the East Coast of Peninsular Malaysia, based on PL-moments is the GLO distribution. 1-4 41st International Conference of Science, Technology, Engineering and Management (ICSTEM) Sydney, Australia
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| spellingShingle |
Determining probability Disyribution for Streamflow Regions using Partial L-Moments
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| summary |
An attempt has been made to model the annual maximum streamflow, utilizing the guidelines in the regional flood frequency analysis. The Partial L-moments (PL-moments) at several censoring levels are employed to estimate the regional parameters of three extreme value distributions, namely; generalized extreme value (GEV), generalized logistic (GLO) and generalized Pareto (GPA) distributions. A total number of 18 streamflow stations located throughout the eastern region of Peninsular Malaysia were used as a case study. Firstly, the data is screening out for data verification and quality control. Next, identification of homogeneous regions is made using homogeneity test based on PL-moments. The PL - diagram is then constructed and GEV and GLO distributions appeared to be the acceptable distributions for representing the regional data. However, it is relatively difficult to identify a particular distribution that most fitted the regional data. Thus, goodness-of-fit test (Z-test) is used and the result showed that the most appropriate distribution for modeling maximum streamflow in the East Coast of Peninsular Malaysia, based on PL-moments is the GLO distribution.
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| title |
Determining probability Disyribution for Streamflow Regions using Partial L-Moments
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| title_full |
Determining probability Disyribution for Streamflow Regions using Partial L-Moments
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| title_fullStr |
Determining probability Disyribution for Streamflow Regions using Partial L-Moments
|
| title_full_unstemmed |
Determining probability Disyribution for Streamflow Regions using Partial L-Moments
|
| title_short |
Determining probability Disyribution for Streamflow Regions using Partial L-Moments
|
| title_sort |
determining probability disyribution for streamflow regions using partial l-moments
|