Fit of probability distributions to monthly precipitation in the state of Pernambuco – Brazil

Detalhes bibliográficos
Autor(a) principal: Ximenes, Patricia de Souza Medeiros Pina
Data de Publicação: 2020
Outros Autores: Silva, Antonio Samuel Alves da, Ashkar, Fahim, Stosic, Tatijana
Tipo de documento: Artigo
Idioma: por
Título da fonte: Research, Society and Development
Texto Completo: https://rsdjournal.org/index.php/rsd/article/view/9894
Resumo: This study aimed to identify probability distribution that best fit monthly rainfall data for the state of Pernambuco - Brazil. The fits of six 2-parameters probability distributions were analyzed: gamma (GAM), log normal (LNORM), Weibull (WEI), Generalized Pareto (GP), Gumbel (GUM) and normal (NORM) for monthly rainfall data of 40 rainfall stations across the state of Pernambuco, from 1988 to 2017 (30 years). The Maximum Likelihood (ML) method was used to estimate the model parameters and the model selection was based on a modification of the Shapiro-Wilk statistic. The results showed the 2-parameters distributions are flexible enough to describe monthly precipitation data for the state of Pernambuco and the log normal, gamma, Weibull and GP models fitted better to the data. The Gumbel and normal models rarely adjusted to the data regardless of the month analyzed.
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spelling Fit of probability distributions to monthly precipitation in the state of Pernambuco – BrazilAjuste de las distribuciones de probabilidad a la precipitación mensual en el estado de Pernambuco – BrasilAjuste de distribuições de probabilidade à precipitação mensal no estado de Pernambuco – BrasilMonthly rainfallPernambucoProbability distributions.Precipitación mensualPernambucoDistribuciones de probabilidad.Precipitação mensalPernambucoDistribuições de probabilidade.This study aimed to identify probability distribution that best fit monthly rainfall data for the state of Pernambuco - Brazil. The fits of six 2-parameters probability distributions were analyzed: gamma (GAM), log normal (LNORM), Weibull (WEI), Generalized Pareto (GP), Gumbel (GUM) and normal (NORM) for monthly rainfall data of 40 rainfall stations across the state of Pernambuco, from 1988 to 2017 (30 years). The Maximum Likelihood (ML) method was used to estimate the model parameters and the model selection was based on a modification of the Shapiro-Wilk statistic. The results showed the 2-parameters distributions are flexible enough to describe monthly precipitation data for the state of Pernambuco and the log normal, gamma, Weibull and GP models fitted better to the data. The Gumbel and normal models rarely adjusted to the data regardless of the month analyzed.Este estudio tuvo como objetivo identificar los modelos de distribución de probabilidad que mejor se ajustan a los datos de precipitación mensual para el estado de Pernambuco - Brasil. Se analizaron los ajustes de seis distribuciones de probabilidad de 2 parámetros: gamma (GAM), log normal (LNORM), Weibull (WEI), Pareto generalizado (PG), Gumbel (GUM) y normal (NORM) para los datos de precipitación mensual. 40 estaciones pluviométricas distribuidas en el estado de Pernambuco, en el período de 1988 - 2017 (30 años). Se utilizó el método de máxima verosimilitud (ML) para estimar los parámetros del modelo y la selección del modelo se basó en una modificación del estadístico de Shapiro-Wilk. Los resultados mostraron que las distribuciones de 2 parámetros son lo suficientemente flexibles para describir los datos de precipitación mensual para el estado de Pernambuco y que los modelos log normal, gamma, Weibull y PG se ajustan mejor a los datos. Los modelos Gumbel y normal rara vez se ajustan a los datos independientemente del mes analizado.Este estudo teve como objetivo identificar modelos de distribuição de probabilidade que melhor se ajustam a dados de precipitação mensal para o estado de Pernambuco – Brasil. Foram analisados os ajustes de seis distribuições de probabilidade de 2 parâmetros: gama (GAM), log normal (LNORM), Weibull (WEI), Pareto Generalizado (PG), Gumbel (GUM) e normal (NORM) para dados de precipitação mensal de 40 estações pluviométricas distribuídas no estado de Pernambuco, no período de 1988 a 2017 (30 anos). O método de Máxima Verossimilhança (ML) foi utilizado para estimar os parâmetros dos modelos e a seleção do modelo baseou-se em uma modificação da estatística de Shapiro-Wilk. Os resultados mostraram que as distribuições de 2 parâmetros são flexíveis o suficiente para descrever dados de precipitação mensal para o estado de Pernambuco e que os modelos log normal, gama, Weibull e PG se ajustaram melhor aos dados. Os modelos Gumbel e normal raramente se ajustaram aos dados independente do mês analisado.Research, Society and Development2020-11-22info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/989410.33448/rsd-v9i11.9894Research, Society and Development; Vol. 9 No. 11; e4869119894Research, Society and Development; Vol. 9 Núm. 11; e4869119894Research, Society and Development; v. 9 n. 11; e48691198942525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIporhttps://rsdjournal.org/index.php/rsd/article/view/9894/9020Copyright (c) 2020 Patricia de Souza Medeiros Pina Ximenes; Antonio Samuel Alves da Silva; Fahim Ashkar; Tatijana Stosichttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessXimenes, Patricia de Souza Medeiros Pina Silva, Antonio Samuel Alves daAshkar, FahimStosic, Tatijana2020-12-10T23:37:57Zoai:ojs.pkp.sfu.ca:article/9894Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:32:04.319416Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false
dc.title.none.fl_str_mv Fit of probability distributions to monthly precipitation in the state of Pernambuco – Brazil
Ajuste de las distribuciones de probabilidad a la precipitación mensual en el estado de Pernambuco – Brasil
Ajuste de distribuições de probabilidade à precipitação mensal no estado de Pernambuco – Brasil
title Fit of probability distributions to monthly precipitation in the state of Pernambuco – Brazil
spellingShingle Fit of probability distributions to monthly precipitation in the state of Pernambuco – Brazil
Ximenes, Patricia de Souza Medeiros Pina
Monthly rainfall
Pernambuco
Probability distributions.
Precipitación mensual
Pernambuco
Distribuciones de probabilidad.
Precipitação mensal
Pernambuco
Distribuições de probabilidade.
title_short Fit of probability distributions to monthly precipitation in the state of Pernambuco – Brazil
title_full Fit of probability distributions to monthly precipitation in the state of Pernambuco – Brazil
title_fullStr Fit of probability distributions to monthly precipitation in the state of Pernambuco – Brazil
title_full_unstemmed Fit of probability distributions to monthly precipitation in the state of Pernambuco – Brazil
title_sort Fit of probability distributions to monthly precipitation in the state of Pernambuco – Brazil
author Ximenes, Patricia de Souza Medeiros Pina
author_facet Ximenes, Patricia de Souza Medeiros Pina
Silva, Antonio Samuel Alves da
Ashkar, Fahim
Stosic, Tatijana
author_role author
author2 Silva, Antonio Samuel Alves da
Ashkar, Fahim
Stosic, Tatijana
author2_role author
author
author
dc.contributor.author.fl_str_mv Ximenes, Patricia de Souza Medeiros Pina
Silva, Antonio Samuel Alves da
Ashkar, Fahim
Stosic, Tatijana
dc.subject.por.fl_str_mv Monthly rainfall
Pernambuco
Probability distributions.
Precipitación mensual
Pernambuco
Distribuciones de probabilidad.
Precipitação mensal
Pernambuco
Distribuições de probabilidade.
topic Monthly rainfall
Pernambuco
Probability distributions.
Precipitación mensual
Pernambuco
Distribuciones de probabilidad.
Precipitação mensal
Pernambuco
Distribuições de probabilidade.
description This study aimed to identify probability distribution that best fit monthly rainfall data for the state of Pernambuco - Brazil. The fits of six 2-parameters probability distributions were analyzed: gamma (GAM), log normal (LNORM), Weibull (WEI), Generalized Pareto (GP), Gumbel (GUM) and normal (NORM) for monthly rainfall data of 40 rainfall stations across the state of Pernambuco, from 1988 to 2017 (30 years). The Maximum Likelihood (ML) method was used to estimate the model parameters and the model selection was based on a modification of the Shapiro-Wilk statistic. The results showed the 2-parameters distributions are flexible enough to describe monthly precipitation data for the state of Pernambuco and the log normal, gamma, Weibull and GP models fitted better to the data. The Gumbel and normal models rarely adjusted to the data regardless of the month analyzed.
publishDate 2020
dc.date.none.fl_str_mv 2020-11-22
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://rsdjournal.org/index.php/rsd/article/view/9894
10.33448/rsd-v9i11.9894
url https://rsdjournal.org/index.php/rsd/article/view/9894
identifier_str_mv 10.33448/rsd-v9i11.9894
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://rsdjournal.org/index.php/rsd/article/view/9894/9020
dc.rights.driver.fl_str_mv https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Research, Society and Development
publisher.none.fl_str_mv Research, Society and Development
dc.source.none.fl_str_mv Research, Society and Development; Vol. 9 No. 11; e4869119894
Research, Society and Development; Vol. 9 Núm. 11; e4869119894
Research, Society and Development; v. 9 n. 11; e4869119894
2525-3409
reponame:Research, Society and Development
instname:Universidade Federal de Itajubá (UNIFEI)
instacron:UNIFEI
instname_str Universidade Federal de Itajubá (UNIFEI)
instacron_str UNIFEI
institution UNIFEI
reponame_str Research, Society and Development
collection Research, Society and Development
repository.name.fl_str_mv Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)
repository.mail.fl_str_mv rsd.articles@gmail.com
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