Análise Bayesiana da precipitação máxima de Petrópolis-RJ
| Ano de defesa: | 2021 |
|---|---|
| Autor(a) principal: | |
| Orientador(a): | |
| Banca de defesa: | , |
| Tipo de documento: | Dissertação |
| Tipo de acesso: | Acesso aberto |
| Idioma: | por |
| Instituição de defesa: |
Universidade Federal de Alfenas
|
| Programa de Pós-Graduação: |
Programa de Pós-Graduação em Estatística Aplicada e Biometria
|
| Departamento: |
Instituto de Ciências Exatas
|
| País: |
Brasil
|
| Palavras-chave em Português: | |
| Área do conhecimento CNPq: | |
| Link de acesso: | https://repositorio.unifal-mg.edu.br/handle/123456789/1926 |
Resumo: | The city of Petrópolis, located in the mountainous region of the state of Rio de Janeiro (RJ), Brazil, frequently suffers from damage caused by heavy rains, such as those oc- curred in 2011 and 2013. Therefore, analyze and predict the occurrence of extreme rainfall in Petrópolis is fundamental for planning activities vulnerable to their occur- rence, such as farming and and the removal of people from risk areas, the first being fundamental for the livelihoods of many local families and the second being extremely important to avoid the loss of human life. The modeling of the behavior of this extreme event is usually done through the Generalized Distribution of Extreme Values (GEV). The Bayesian methodology has shown good results in estimating the parameters of the GEV distribution. Thus, the purpose was to adjust the GEV distribution to the historical series of maximum rainfall in Petrópolis, and to evaluate different structures of prioris, informative and non-informative in the forecasting of extreme rainfall values in Petrópo- lis at different return times. It was possible to conclude that the priori distribution based on information from the city of Teresópolis, given the variance, provided more accurate and accurate results in the prediction of return levels for the city of Petrópolis. In this way, forecasts for the maximum rainfall expected for the return times of 2, 5, 10, 25, 50, 100, 115 and 135 years were made using this priori structure. It is expected that, in an average time of 5 years, there will be at least one day with maximum rainfall greater than or equal to 100.7mm in Petrópolis. |
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Silva, Sandra Valéria Coelho Dahttp://lattes.cnpq.br/8194104388434526Muniz, Joel AugustoGonçalves, Kelly Cristina MotaAvelar, Fabrício GoeckingBeijo, Luiz Albertohttp://lattes.cnpq.br/03980437092899582022-01-27T14:44:45Z2021-07-29SILVA, Sandra Valéria Coelho da. Análise Bayesiana da precipitação máxima de Petrópolis-RJ. 2021. 44 f. Dissertação (Mestrado em Estatística Aplicada e Biometria) - Universidade Federal de Alfenas, Alfenas, MG, 2021.https://repositorio.unifal-mg.edu.br/handle/123456789/1926The city of Petrópolis, located in the mountainous region of the state of Rio de Janeiro (RJ), Brazil, frequently suffers from damage caused by heavy rains, such as those oc- curred in 2011 and 2013. Therefore, analyze and predict the occurrence of extreme rainfall in Petrópolis is fundamental for planning activities vulnerable to their occur- rence, such as farming and and the removal of people from risk areas, the first being fundamental for the livelihoods of many local families and the second being extremely important to avoid the loss of human life. The modeling of the behavior of this extreme event is usually done through the Generalized Distribution of Extreme Values (GEV). The Bayesian methodology has shown good results in estimating the parameters of the GEV distribution. Thus, the purpose was to adjust the GEV distribution to the historical series of maximum rainfall in Petrópolis, and to evaluate different structures of prioris, informative and non-informative in the forecasting of extreme rainfall values in Petrópo- lis at different return times. It was possible to conclude that the priori distribution based on information from the city of Teresópolis, given the variance, provided more accurate and accurate results in the prediction of return levels for the city of Petrópolis. In this way, forecasts for the maximum rainfall expected for the return times of 2, 5, 10, 25, 50, 100, 115 and 135 years were made using this priori structure. It is expected that, in an average time of 5 years, there will be at least one day with maximum rainfall greater than or equal to 100.7mm in Petrópolis.A cidade de Petrópolis, situada na região serrana do estado do Rio de Janeiro-RJ, sofre frequentemente com estragos provocados por fortes chuvas, como as ocorridas nos anos de 2011 e 2013. Portanto, analisar e prever a ocorrência de precipitações pluviais extremas em Petrópolis é fundamental para o planejamento de atividades vul- neráveis a sua ocorrência, tais como a agropecuária e a remoção de pessoas das áreas de risco, sendo a primeira fundamental para a subsistência de muitas famílias locais e a segunda de extrema importância para evitar a perda de vidas humanas. A modelagem do comportamento desse evento extremo é feita, geralmente, por meio da distribuição Generalizada de Valores Extremos (GEV). A metodologia Bayesiana tem apresentado bons resultados na estimação dos parâmetros da distribuição GEV. Sendo assim, objetivou-se ajustar a distribuição GEV às séries históricas de precipi- tação máxima de Petrópolis, e avaliar diferentes estruturas de prioris, informativas e não informativas, na previsão dos valores de precipitação extrema de Petrópolis em diferentes tempos de retorno. Foi possível concluir que a distribuição a priori funda- mentada em informações da cidade de Teresópolis, uma vez a variância, forneceu resultados mais precisos e acurados na predição dos níveis de retorno para a cidade de Petrópolis. Dessa forma, foram realizadas as predições para as precipitações má- ximas esperadas para os tempos de retorno de 2, 5, 10, 25, 50, 100, 115 e 135 anos utilizando-se essa estrutura de priori. Espera-se que, em um tempo médio de 5 anos, ocorra pelo menos um dia com precipitação máxima maior ou igual 100,7mm em Petrópolis-RJ.application/pdfporUniversidade Federal de AlfenasPrograma de Pós-Graduação em Estatística Aplicada e BiometriaUNIFAL-MGBrasilInstituto de Ciências Exatasinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/Chuva extremaDistribuição GEVPriori informativaTempos de retorno.CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICAAnálise Bayesiana da precipitação máxima de Petrópolis-RJinfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/publishedVersion-8156311678363143599600600-5836407828185143517reponame:Repositório Institucional da Universidade Federal de Alfenas - RiUnifalinstname:Universidade Federal de Alfenas (UNIFAL)instacron:UNIFALSilva, Sandra Valéria Coelho DaLICENSElicense.txtlicense.txttext/plain; charset=utf-81987https://repositorio.unifal-mg.edu.br/bitstreams/91a4981a-d305-420e-8c2a-d354e44cf98e/download31555718c4fc75849dd08f27935d4f6bMD51CC-LICENSElicense_urllicense_urltext/plain; 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| dc.title.pt-BR.fl_str_mv |
Análise Bayesiana da precipitação máxima de Petrópolis-RJ |
| title |
Análise Bayesiana da precipitação máxima de Petrópolis-RJ |
| spellingShingle |
Análise Bayesiana da precipitação máxima de Petrópolis-RJ Silva, Sandra Valéria Coelho Da Chuva extrema Distribuição GEV Priori informativa Tempos de retorno. CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA |
| title_short |
Análise Bayesiana da precipitação máxima de Petrópolis-RJ |
| title_full |
Análise Bayesiana da precipitação máxima de Petrópolis-RJ |
| title_fullStr |
Análise Bayesiana da precipitação máxima de Petrópolis-RJ |
| title_full_unstemmed |
Análise Bayesiana da precipitação máxima de Petrópolis-RJ |
| title_sort |
Análise Bayesiana da precipitação máxima de Petrópolis-RJ |
| author |
Silva, Sandra Valéria Coelho Da |
| author_facet |
Silva, Sandra Valéria Coelho Da |
| author_role |
author |
| dc.contributor.author.fl_str_mv |
Silva, Sandra Valéria Coelho Da |
| dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/8194104388434526 |
| dc.contributor.advisor-co1.fl_str_mv |
Muniz, Joel Augusto |
| dc.contributor.referee1.fl_str_mv |
Gonçalves, Kelly Cristina Mota |
| dc.contributor.referee2.fl_str_mv |
Avelar, Fabrício Goecking |
| dc.contributor.advisor1.fl_str_mv |
Beijo, Luiz Alberto |
| dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/0398043709289958 |
| contributor_str_mv |
Muniz, Joel Augusto Gonçalves, Kelly Cristina Mota Avelar, Fabrício Goecking Beijo, Luiz Alberto |
| dc.subject.por.fl_str_mv |
Chuva extrema Distribuição GEV Priori informativa Tempos de retorno. |
| topic |
Chuva extrema Distribuição GEV Priori informativa Tempos de retorno. CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA |
| dc.subject.cnpq.fl_str_mv |
CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA |
| description |
The city of Petrópolis, located in the mountainous region of the state of Rio de Janeiro (RJ), Brazil, frequently suffers from damage caused by heavy rains, such as those oc- curred in 2011 and 2013. Therefore, analyze and predict the occurrence of extreme rainfall in Petrópolis is fundamental for planning activities vulnerable to their occur- rence, such as farming and and the removal of people from risk areas, the first being fundamental for the livelihoods of many local families and the second being extremely important to avoid the loss of human life. The modeling of the behavior of this extreme event is usually done through the Generalized Distribution of Extreme Values (GEV). The Bayesian methodology has shown good results in estimating the parameters of the GEV distribution. Thus, the purpose was to adjust the GEV distribution to the historical series of maximum rainfall in Petrópolis, and to evaluate different structures of prioris, informative and non-informative in the forecasting of extreme rainfall values in Petrópo- lis at different return times. It was possible to conclude that the priori distribution based on information from the city of Teresópolis, given the variance, provided more accurate and accurate results in the prediction of return levels for the city of Petrópolis. In this way, forecasts for the maximum rainfall expected for the return times of 2, 5, 10, 25, 50, 100, 115 and 135 years were made using this priori structure. It is expected that, in an average time of 5 years, there will be at least one day with maximum rainfall greater than or equal to 100.7mm in Petrópolis. |
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2021 |
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2021-07-29 |
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2022-01-27T14:44:45Z |
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info:eu-repo/semantics/masterThesis |
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SILVA, Sandra Valéria Coelho da. Análise Bayesiana da precipitação máxima de Petrópolis-RJ. 2021. 44 f. Dissertação (Mestrado em Estatística Aplicada e Biometria) - Universidade Federal de Alfenas, Alfenas, MG, 2021. |
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https://repositorio.unifal-mg.edu.br/handle/123456789/1926 |
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SILVA, Sandra Valéria Coelho da. Análise Bayesiana da precipitação máxima de Petrópolis-RJ. 2021. 44 f. Dissertação (Mestrado em Estatística Aplicada e Biometria) - Universidade Federal de Alfenas, Alfenas, MG, 2021. |
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