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Comparison of GEV and Gumble’s distribution for Development of Intensity Duration Frequency Curve for Flood Prone Area in Sabah

Rainfall data has a significant role in hydrological design which is, it’s produce the intensity duration frequency curve. IDF curve gives critical information that needed in the design of water management infrastructure, it gives information by showing the mathematical relation of rainfall intensit...

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Main Authors: Nicklos Jefrin, Nurmin Bolong, Justin Sentian, Ismail Abustan, Thamer Ahmad Mohammad, Janice Lynn Ayog
Format: Article
Language:English
Published: 2018
Subjects:
T Technology (General)
Online Access:https://eprints.ums.edu.my/id/eprint/24190/1/Comparison%20of%20GEV%20and%20Gumble.pdf
https://eprints.ums.edu.my/id/eprint/24190/
https://doi.org/10.26480/mjg.01.2018.42.44
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spelling my.ums.eprints.241902019-11-25T06:51:04Z https://eprints.ums.edu.my/id/eprint/24190/ Comparison of GEV and Gumble’s distribution for Development of Intensity Duration Frequency Curve for Flood Prone Area in Sabah Nicklos Jefrin Nurmin Bolong Justin Sentian Ismail Abustan Thamer Ahmad Mohammad Janice Lynn Ayog T Technology (General) Rainfall data has a significant role in hydrological design which is, it’s produce the intensity duration frequency curve. IDF curve gives critical information that needed in the design of water management infrastructure, it gives information by showing the mathematical relation of rainfall intensity, recurrence interval of the storm and duration of storm. This paper aims to compares and develop IDF curve using two frequency distribution which is generalized extreme value distribution (GEV) and Gumbel distribution (EV1). Then, the best fit distribution for flood-prone area in Sabah will be choose and determined from the two-mentioned distribution. The goodness of fit test that used to determine the best distribution is chi-square test, it works by determining the differences between observe data value from Weibull formula and the estimated values from GEV and Gumbel’s distribution method. After that the chi-square value for GEV and Gumbel is compared to the critical value from chi-square table at significant level of 5%. From the Chi-square test, it is concluded that Gumbel’s (chi square value Tandek:0.47952, patiu:1.0531, babagon: 1.026931, Ulu Moyog:0.382415) shows a better fit distribution compared to GEV distribution (chi square value Tandek:59.7598, patiu:16.5746, babagon: 3.3555347, Ulu Moyog:22.1315) 2018 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/24190/1/Comparison%20of%20GEV%20and%20Gumble.pdf Nicklos Jefrin and Nurmin Bolong and Justin Sentian and Ismail Abustan and Thamer Ahmad Mohammad and Janice Lynn Ayog (2018) Comparison of GEV and Gumble’s distribution for Development of Intensity Duration Frequency Curve for Flood Prone Area in Sabah. Malaysian Journal of Geosciences (MJG), 2 (1). pp. 42-44. ISSN 2521-0920 https://doi.org/10.26480/mjg.01.2018.42.44
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Nicklos Jefrin
Nurmin Bolong
Justin Sentian
Ismail Abustan
Thamer Ahmad Mohammad
Janice Lynn Ayog
Comparison of GEV and Gumble’s distribution for Development of Intensity Duration Frequency Curve for Flood Prone Area in Sabah
description Rainfall data has a significant role in hydrological design which is, it’s produce the intensity duration frequency curve. IDF curve gives critical information that needed in the design of water management infrastructure, it gives information by showing the mathematical relation of rainfall intensity, recurrence interval of the storm and duration of storm. This paper aims to compares and develop IDF curve using two frequency distribution which is generalized extreme value distribution (GEV) and Gumbel distribution (EV1). Then, the best fit distribution for flood-prone area in Sabah will be choose and determined from the two-mentioned distribution. The goodness of fit test that used to determine the best distribution is chi-square test, it works by determining the differences between observe data value from Weibull formula and the estimated values from GEV and Gumbel’s distribution method. After that the chi-square value for GEV and Gumbel is compared to the critical value from chi-square table at significant level of 5%. From the Chi-square test, it is concluded that Gumbel’s (chi square value Tandek:0.47952, patiu:1.0531, babagon: 1.026931, Ulu Moyog:0.382415) shows a better fit distribution compared to GEV distribution (chi square value Tandek:59.7598, patiu:16.5746, babagon: 3.3555347, Ulu Moyog:22.1315)
format Article
author Nicklos Jefrin
Nurmin Bolong
Justin Sentian
Ismail Abustan
Thamer Ahmad Mohammad
Janice Lynn Ayog
author_facet Nicklos Jefrin
Nurmin Bolong
Justin Sentian
Ismail Abustan
Thamer Ahmad Mohammad
Janice Lynn Ayog
author_sort Nicklos Jefrin
title Comparison of GEV and Gumble’s distribution for Development of Intensity Duration Frequency Curve for Flood Prone Area in Sabah
title_short Comparison of GEV and Gumble’s distribution for Development of Intensity Duration Frequency Curve for Flood Prone Area in Sabah
title_full Comparison of GEV and Gumble’s distribution for Development of Intensity Duration Frequency Curve for Flood Prone Area in Sabah
title_fullStr Comparison of GEV and Gumble’s distribution for Development of Intensity Duration Frequency Curve for Flood Prone Area in Sabah
title_full_unstemmed Comparison of GEV and Gumble’s distribution for Development of Intensity Duration Frequency Curve for Flood Prone Area in Sabah
title_sort comparison of gev and gumble’s distribution for development of intensity duration frequency curve for flood prone area in sabah
publishDate 2018
url https://eprints.ums.edu.my/id/eprint/24190/1/Comparison%20of%20GEV%20and%20Gumble.pdf
https://eprints.ums.edu.my/id/eprint/24190/
https://doi.org/10.26480/mjg.01.2018.42.44
_version_ 1760230209062174720
score 13.211869

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