Fit of probability distributions to monthly precipitation in the state of Pernambuco – Brazil
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
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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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 |
_version_ |
1797052742647152640 |