Use of gap filling methodologies to estimate rainfall data

Detalhes bibliográficos
Autor(a) principal: Dias, Allana Siqueira
Data de Publicação: 2021
Outros Autores: Soares, Willames de Albuquerque
Tipo de documento: Artigo
Idioma: por
Título da fonte: Research, Society and Development
Texto Completo: https://rsdjournal.org/index.php/rsd/article/view/15383
Resumo: In the following paper, gap filling methodologies of historical series of rainfall data were applied, not as an auxiliary tool to fill gaps provoked by mistakes, but as a way to fully predict rainfall data for the city of Catanduva, based on data of the cities of São Carlos (SC), Franca (FR) e Votuporanga (VT), located in São Paulo state. Aiming to analyze and evaluate the viability of using the methods of regional weighting, simple linear regression e multiple linear regression, verifying the performance and correlation between estimated and real data (registered by Catanduva’s pluviometric station). There were used historical series information with 30 years of monthly observations provided by the Instituto Nacional de Meteorologia (INMET) to apply the methods mentioned. Comparing the estimated with the measured data, it is possible to affirm that the method of multiple linear regression was the one that best simulated the reality of the observed events and the estimations have a better performance for driest months and when there are used rainfall data from nearby regions.
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spelling Use of gap filling methodologies to estimate rainfall dataUso de metodologías de llenado de fallas para estimar los datos de precipitación Uso de metodologias de preenchimento de falhas para estimativas de dados de precipitaçãoRegional weightingLinear regressionHistorical series.Ponderación regionalRegresión linealSeries históricas.Ponderação regionalRegressão linearSérie histórica.In the following paper, gap filling methodologies of historical series of rainfall data were applied, not as an auxiliary tool to fill gaps provoked by mistakes, but as a way to fully predict rainfall data for the city of Catanduva, based on data of the cities of São Carlos (SC), Franca (FR) e Votuporanga (VT), located in São Paulo state. Aiming to analyze and evaluate the viability of using the methods of regional weighting, simple linear regression e multiple linear regression, verifying the performance and correlation between estimated and real data (registered by Catanduva’s pluviometric station). There were used historical series information with 30 years of monthly observations provided by the Instituto Nacional de Meteorologia (INMET) to apply the methods mentioned. Comparing the estimated with the measured data, it is possible to affirm that the method of multiple linear regression was the one that best simulated the reality of the observed events and the estimations have a better performance for driest months and when there are used rainfall data from nearby regions.En el presente trabajo, se utilizaron las metodologías para llenar vacíos en series de tiempo de precipitación, no como una herramienta auxiliar para llenar los vacíos causados por errores, sino como un medio para predecir completamente los datos de precipitación para el municipio de Catanduva, con base en sobre datos de los municipios de São Carlos (SC), Franca (FR) y Votuporanga (VT), ubicados en el estado de São Paulo. Así, con el objetivo de analizar y evaluar la viabilidad de utilizar los métodos de ponderación regional, regresión lineal simple y regresión lineal múltiple, verificando el desempeño y la correlación entre los datos estimados y los datos reales (registrados por la estación de precipitación de Catanduva). Para la aplicación de los métodos mencionados se utilizó información de series históricas con 30 años de observaciones mensuales proporcionadas por el Instituto Nacional de Meteorología (INMET). Comparando los datos estimados con los datos medidos, se puede decir que el método de regresión lineal múltiple fue el que mejor simuló la realidad de los eventos observados y que las estimaciones funcionan mejor para los meses menos lluviosos y cuando los datos de precipitación de regiones cercanas son usó.No presente trabalho, as metodologias para preenchimento de falhas em séries temporais de precipitação foram utilizadas, não como ferramenta auxiliar no preenchimento de lacunas provocadas por erros, mas sim como um meio de prever integralmente os dados de pluviometria para o município de Catanduva, baseando-se em dados dos municípios de São Carlos (SC), Franca (FR) e Votuporanga (VT), localizados no estado de São Paulo. Objetivando assim, analisar e avaliar a viabilidade do uso dos métodos da ponderação regional, da regressão linear simples e da regressão linear múltipla, verificando o desempenho e a correlação entre os dados estimados e os dados reais (registrados pelo posto pluviométrico de Catanduva). Foram utilizadas informações de séries históricas com 30 anos de observações mensais fornecidas pelo Instituto Nacional de Meteorologia (INMET) para a aplicação dos métodos citados. Comparando os dados estimados aos dados medidos pode-se afirmar que o método da regressão linear múltipla foi o que melhor simulou a realidade dos eventos observados e que as estimativas têm melhor desempenho para os meses menos chuvosos e quando são utilizados dados pluviométricos de regiões próximas.Research, Society and Development2021-05-17info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/1538310.33448/rsd-v10i5.15383Research, Society and Development; Vol. 10 No. 5; e57610515383Research, Society and Development; Vol. 10 Núm. 5; e57610515383Research, Society and Development; v. 10 n. 5; e576105153832525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIporhttps://rsdjournal.org/index.php/rsd/article/view/15383/13732Copyright (c) 2021 Allana Siqueira Dias; Willames de Albuquerque Soareshttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessDias, Allana SiqueiraSoares, Willames de Albuquerque2021-05-17T18:20:49Zoai:ojs.pkp.sfu.ca:article/15383Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:36:13.223507Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false
dc.title.none.fl_str_mv Use of gap filling methodologies to estimate rainfall data
Uso de metodologías de llenado de fallas para estimar los datos de precipitación
Uso de metodologias de preenchimento de falhas para estimativas de dados de precipitação
title Use of gap filling methodologies to estimate rainfall data
spellingShingle Use of gap filling methodologies to estimate rainfall data
Dias, Allana Siqueira
Regional weighting
Linear regression
Historical series.
Ponderación regional
Regresión lineal
Series históricas.
Ponderação regional
Regressão linear
Série histórica.
title_short Use of gap filling methodologies to estimate rainfall data
title_full Use of gap filling methodologies to estimate rainfall data
title_fullStr Use of gap filling methodologies to estimate rainfall data
title_full_unstemmed Use of gap filling methodologies to estimate rainfall data
title_sort Use of gap filling methodologies to estimate rainfall data
author Dias, Allana Siqueira
author_facet Dias, Allana Siqueira
Soares, Willames de Albuquerque
author_role author
author2 Soares, Willames de Albuquerque
author2_role author
dc.contributor.author.fl_str_mv Dias, Allana Siqueira
Soares, Willames de Albuquerque
dc.subject.por.fl_str_mv Regional weighting
Linear regression
Historical series.
Ponderación regional
Regresión lineal
Series históricas.
Ponderação regional
Regressão linear
Série histórica.
topic Regional weighting
Linear regression
Historical series.
Ponderación regional
Regresión lineal
Series históricas.
Ponderação regional
Regressão linear
Série histórica.
description In the following paper, gap filling methodologies of historical series of rainfall data were applied, not as an auxiliary tool to fill gaps provoked by mistakes, but as a way to fully predict rainfall data for the city of Catanduva, based on data of the cities of São Carlos (SC), Franca (FR) e Votuporanga (VT), located in São Paulo state. Aiming to analyze and evaluate the viability of using the methods of regional weighting, simple linear regression e multiple linear regression, verifying the performance and correlation between estimated and real data (registered by Catanduva’s pluviometric station). There were used historical series information with 30 years of monthly observations provided by the Instituto Nacional de Meteorologia (INMET) to apply the methods mentioned. Comparing the estimated with the measured data, it is possible to affirm that the method of multiple linear regression was the one that best simulated the reality of the observed events and the estimations have a better performance for driest months and when there are used rainfall data from nearby regions.
publishDate 2021
dc.date.none.fl_str_mv 2021-05-17
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/15383
10.33448/rsd-v10i5.15383
url https://rsdjournal.org/index.php/rsd/article/view/15383
identifier_str_mv 10.33448/rsd-v10i5.15383
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://rsdjournal.org/index.php/rsd/article/view/15383/13732
dc.rights.driver.fl_str_mv Copyright (c) 2021 Allana Siqueira Dias; Willames de Albuquerque Soares
https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2021 Allana Siqueira Dias; Willames de Albuquerque Soares
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. 10 No. 5; e57610515383
Research, Society and Development; Vol. 10 Núm. 5; e57610515383
Research, Society and Development; v. 10 n. 5; e57610515383
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_ 1797052806777012224

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