Estimating distribution parameters of extreme event using TL-moment method
Also available in printed version : QA273.6 N36 2014 raf
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | |
| التنسيق: | Master's thesis |
| منشور في: |
Universiti Teknologi Malaysia
2025
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://utmik.utm.my/handle/123456789/101698 |
| Abstract | Abstract here |
| _version_ | 1854934146365784064 |
|---|---|
| author | Nur Amalina Mat Jan |
| author2 | Ani Shabri, supervisor |
| author_facet | Ani Shabri, supervisor Nur Amalina Mat Jan |
| author_sort | Nur Amalina Mat Jan |
| description | Also available in printed version : QA273.6 N36 2014 raf |
| format | Master's thesis |
| id | utm-123456789-101698 |
| institution | Universiti Teknologi Malaysia |
| publishDate | 2025 |
| publisher | Universiti Teknologi Malaysia |
| record_format | dspace |
| record_pdf | Abstract |
| spelling | utm-123456789-1016982025-08-21T09:43:21Z Estimating distribution parameters of extreme event using TL-moment method Nur Amalina Mat Jan Ani Shabri, supervisor Distribution (Probability theory) Monte Carlo method Also available in printed version : QA273.6 N36 2014 raf Good knowledge of flood magnitude and frequency is necessary in designing different types of flood protection projects. The use of flood frequency analysis (FFA) can help hydrologists in mitigating the problem of extreme flooding. The main problem in hydrologic design faced by the hydrologists is the estimation of high flow quantile. L-Moments, popular among hydrologist in FFA, is said to be over sensitive towards the lower part of the distribution and gives insufficient weight to large sample values. As an alternative, the trimmed L-Moments (TL-moments) method is proposed to be used in FFA since it has the ability to give zero weight on the extreme values. The aim of this study is to compare the performance of L-moments, TL-moments (1,0), TL-moments (2,0), TL-moments (3,0) and TL-moments (4,0) in FFA application. The four distributions, named generalized logistic (GLO), generalized extreme value (GEV), three parameter lognormal (LN3), and Pearson 3 (P3) distributions, were chosen and an estimation of the distributions using TL-moments (r,0), r = 1, 2, 3, 4 was formulated. The comparison is done using Monte Carlo simulation and annual maximum streamflow data over stations in Peninsular Malaysia. Simulation results show that TL-moments give comparable and better parameter estimates than those by L-moments, particularly when estimating the high flow quantiles. Furthermore, the generalized extreme value (GEV) and three parameters lognormal (LN3) distributions obtained from TL-moments method suit with the actual maximum streamflows of stations in Johor atiff UTM 154 p. Thesis (Sarjana Sains (Matematik)) - Universiti Teknologi Malaysia, 2014 2025-04-10T04:56:02Z 2025-04-10T04:56:02Z 2014 Master's thesis https://utmik.utm.my/handle/123456789/101698 valet-20160127-145857 vital:83871 Closed Access UTM Complete Unpublished application/pdf Universiti Teknologi Malaysia |
| spellingShingle | Distribution (Probability theory) Monte Carlo method Nur Amalina Mat Jan Estimating distribution parameters of extreme event using TL-moment method |
| thesis_level |
Master
|
| title | Estimating distribution parameters of extreme event using TL-moment method |
| title_full | Estimating distribution parameters of extreme event using TL-moment method |
| title_fullStr | Estimating distribution parameters of extreme event using TL-moment method |
| title_full_unstemmed | Estimating distribution parameters of extreme event using TL-moment method |
| title_short | Estimating distribution parameters of extreme event using TL-moment method |
| title_sort | estimating distribution parameters of extreme event using tl moment method |
| topic | Distribution (Probability theory) Monte Carlo method |
| url | https://utmik.utm.my/handle/123456789/101698 |
| work_keys_str_mv | AT nuramalinamatjan estimatingdistributionparametersofextremeeventusingtlmomentmethod |