Modelling Extreme Rainfall using Extended Generalized Extreme Value Distribution

被引：0

作者：

Deetae, N. ^{[1
]}

Khamrot, P. ^{[2
]}

Jampachaisri, K. ^{[3
]}

机构：

[1] Pibulsongkram Rajabhat Univ, Fac Sci & Technol, Dept Stat, Phitsanulok, Thailand

[2] Rajamangala Univ Technol Lanna, Fac Sci & Agr Technol, Dept Math, Phitsanulok, Thailand

[3] Naresuan Univ, Fac Sci, Dept Math, Phitsanulok, Thailand

来源：

INTERNATIONAL JOURNAL OF ANALYSIS AND APPLICATIONS | 2025年 / 23卷

关键词：

extreme value theory; generalized extreme value distribution; kumaraswamy generalized extreme value distribution; return level; maximum likelihood estimation; rainfall;

D O I：

10.28924/2291-8639-23-2025-73

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

This study assesses the performance of extended generalized extreme value (GEV) distribution based on Kumaraswamy generalized extreme value (KumGEV) distribution using the maximum likelihood estimates on extreme rainfall data obtained from a weather station in Phitsanulok province, a total of 408 months during January 1987 to December 2021. The findings indicate that the KumGEV distribution provides a better fit than the traditional GEV distribution, with estimated parameters mu = 41.4966 (SE = 0.6015), sigma = 8.9467 (SE = 0.0797), xi = -0.0502 (SE = 0.0308), a = 0.0310 (SE = 0.0060), and b = 0.2738 (SE = 0.0155). Additionally, the analysis of return levels derived from both GEV and KumGEV distributions shows an upward trend over return periods of 10, 20, 50, and 100 years, highlighting significant changes in rainfall patterns over time.

引用

页数：12

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