The analysis of extreme rainfall events in Pekanbaru city using three-parameter generalized extreme value and generalized Pareto distribution

2018 ◽  
Vol 12 (2) ◽  
pp. 69-80 ◽  
Author(s):  
Wenny Susanti ◽  
Arisman Adnan ◽  
Rado Yendra ◽  
M. N. Muhaijir
Keyword(s):  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Extreme Rainfall ◽  
Extreme Value ◽  
Generalized Extreme Value ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Generalized Pareto

scholarly journals Sebaran Generalized Extreme Value (GEV) Dan Generalized Pareto (GP) untuk Pendugaan Curah Hujan Ekstrim di Wilayah DKI Jakarta

2016 ◽  
Vol 7 (1) ◽  
pp. 75-84
Author(s):  
Achi Rinaldi
Keyword(s):  
Pareto Distribution ◽  
Extreme Event ◽  
Generalized Pareto Distribution ◽  
Extreme Value Distribution ◽  
Location Parameter ◽  
Extreme Rainfall ◽  
Extreme Value ◽  
Generalized Extreme Value Distribution ◽  
Generalized Extreme Value ◽  
Generalized Pareto

Extreme event such as extreme rainfall have been analyzed and most concern for the country all around the world. There are two common distribution for extreme value which are Generalized Extreme Value distribution and Generalized Pareto distribution. These two distribution have shown good performace to estimate the parameter of  extreme value. This research was aim to estimate parameter of extreme value using GEV distribution and GP distribution, and also to characterized effect of extreme event such as flood. The rainfall data was taken from BMKG for 5 location in DKI Jakarta. Both of distribution shown a good perfromance. The resut showed that Tanjung Priok station has biggest location parameter for GEV and also the biggest scale parameter for GP, that mean the biggest probability to take flood effect of the extreme rainfall.

scholarly journals VARIABILIDADE DA ESTIMATIVA DE EVENTOS EXTREMOS DE PRECIPITAÇÃO NO MUNICÍPIO DE MACAÉ – RJ

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Isabor Viana Sant'Ana Mendes ◽  
Aline Míriam Marques ◽  
Danilo De Assis Amaral ◽  
Bruno Luis da Silva Maia ◽  
Flávia Ribeiro Villela Fernandes ◽  
...  
Keyword(s):  
Analysis Of Variance ◽  
Rio De Janeiro ◽  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Extreme Value ◽  
Generalized Extreme Value ◽  
Generalized Pareto ◽  
Significant Difference ◽  
Post Hoc

O estudo de eventos extremos de precipitação torna-se essencial e relevante devido aos impactos e implicações que estes eventos provocam na sociedade. No município de Macaé esses eventos adversos são constantes, sendo o que mais registrou ocorrência de alagamentos caracterizados como desastres no estado do Rio de Janeiro, no período de 1991 a 2012, no Atlas Brasileiro de Desastres Naturais. Assim, o objetivo deste trabalho foi analisar a variabilidade dos valores obtidos através dos métodos Generalized Extreme Value (GEV) e Generalized Pareto Distribution(GPD) para determinação do nível de retorno de eventos extremos de precipitação no município de Macaé. O modelo estatístico Analysis of Variance (ANOVA) foi adotado e o diagnóstico do modelo validado. Adicionalmente, a análise post hoc foi realizada através do Tukey Honest Significant Difference. As análises estatísticas foram implementadas no ambiente de software R. De fato, constata-se que existem diferenças significativas entre as estimativas dos níveis de precipitação segundo métodos e estação do ano. Os resultados podem auxiliar análises futuras e corroboram com a escolha do melhor método para entender a variação e o comportamento de eventos com grandes volumes pluviométricos em curta duração de tempo ou enxurradas. Sendo portanto, um estudo relevante para auxiliar no planejamento e na implementação de ações de políticas públicas a serem aplicadas em problemas oriundos de tais eventos.

scholarly journals Comparison of parameters of the generalized extreme value distribution associated with extreme rainfall events in Central America

Uniciencia ◽  
2020 ◽  
Vol 34 (1) ◽  
pp. 111-128
Author(s):  
Helen S. Guillén-Oviedo ◽  
Luis R. Cid-Serrano ◽  
Eric J. Alfaro-Martínez
Keyword(s):  
Central America ◽  
Hypothesis Test ◽  
Extreme Value Distribution ◽  
Extreme Rainfall ◽  
Extreme Value ◽  
Value Distribution ◽  
Generalized Extreme Value Distribution ◽  
Generalized Extreme Value ◽  
Extreme Rainfall Events ◽  
Rainfall Events

The generalized extreme value distribution (GEVD) was used to model extreme rainfall events in Central America for a period of 30 years, beginning in 1971. Data consisted of daily rainfall records of 103 meteorological stations located throughout the isthmus. Central America was divided into 3 regions: Pacific, Caribbean and an intermediate zone. A bootstrap hypothesis test was used to compare the parameters of location, scale and form of the 3 regions. The results of the study showed significant differences between the 3 regions, mainly in terms of location and scale parameters.

A generalized Pareto distribution–based extreme value model of thermal gradients in a long-span bridge combining parameter updating

2016 ◽  
Vol 20 (2) ◽  
pp. 202-213 ◽  
Author(s):  
Guang-Dong Zhou ◽  
Ting-Hua Yi ◽  
Bin Chen ◽  
Huan Zhang
Keyword(s):  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Extreme Value ◽  
Thermal Gradients ◽  
Long Span Bridges ◽  
Long Span ◽  
Generalized Pareto ◽  
Long Span Bridge ◽  
Extreme Value Model ◽  
Value Model

Estimating extreme value models with high reliability for thermal gradients is a significant task that must be completed before reasonable thermal loads and possible thermal stress in long-span bridges are evaluated. In this article, a generalized Pareto distribution–based extreme value model combining parameter updating has been developed to describe the statistical characteristics of thermal gradients in a long-span bridge. The procedure of excluding correlation and the approach of selecting a proper threshold are suggested to prepare samples for generalized Pareto distribution estimation. A Bayesian estimation, which has the capability of updating model parameters by fusing prior information and incoming monitoring data, is proposed to fit the generalized Pareto distribution–based model. Furthermore, the Gibbs sampling, which is a Markov chain Monte Carlo algorithm, is adopted to derive the Bayesian posterior distribution. Finally, the proposed method is applied to the field monitoring data of thermal gradients in the Jiubao Bridge. The extreme value models of thermal gradients for the Jiubao Bridge are established, and the extreme thermal gradients with different return periods are extrapolated. The results indicate that the generalized Pareto distribution–based extreme value model has a strong ability to represent the statistical features of thermal gradients for the Jiubao Bridge, and the Bayesian estimation combining parameter updating provides high-precision generalized Pareto distribution–based models for predicting extreme thermal gradients. The predicted extreme thermal gradients are expected to evaluate and design long-span bridges.

Rate of convergence for the generalized Pareto approximation of the excesses

2003 ◽  
Vol 35 (04) ◽  
pp. 1007-1027 ◽  
Author(s):  
J.-P. Raoult ◽  
R. Worms
Keyword(s):  
Distribution Function ◽  
Uniform Convergence ◽  
Rate Of Convergence ◽  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Domain Of Attraction ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Value Distribution ◽  
Generalized Pareto

Let F be a distribution function in the domain of attraction of an extreme-value distribution H γ. If F u is the distribution function of the excesses over u and G γ the distribution function of the generalized Pareto distribution, then it is well known that F u (x) converges to G γ(x/σ(u)) as u tends to the end point of F, where σ is an appropriate normalizing function. We study the rate of (uniform) convergence to 0 of F̅ u (x)-G̅γ((x+u-α(u))/σ(u)), where α and σ are two appropriate normalizing functions.

scholarly journals Generalized Pareto distribution applied to the analysis of maximum rainfall events in Uruguaiana, RS, Brazil

2020 ◽  
Vol 2 (9) ◽  
Author(s):  
Amanda Larissa Alves Martins ◽  
Gilberto Rodrigues Liska ◽  
Luiz Alberto Beijo ◽  
Fortunato Silva de Menezes ◽  
Marcelo Ângelo Cirillo
Keyword(s):  
Pareto Distribution ◽  
Generalized Pareto Distribution ◽  
Maximum Rainfall ◽  
Rainfall Events ◽  
Generalized Pareto
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