Gráficos de controle para variáveis ambientais duplamente limitadas e autocorrelacionadas
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Manancial - Repositório Digital da UFSM |
Texto Completo: | http://repositorio.ufsm.br/handle/1/22218 |
Resumo: | In the environmental field, especially in hydrology, several activities require monitoring tools to assist in the process of decision making. Control chart is a statistical process control (SPC) tool that can be used for this purpose. However, one of the assumptions made for its use is the independence between different observations. In some processes, this assumption could not be verified, as in hydrological time series, which reduces the applicability of the usual control charts. A solution for this can be given by monitoring the residuals of a fitted time series model, such as, the Kumaraswamy autoregressive moving averages (KARMA) model, which was recently proposed for modeling double bounded environmental time series. In this context, this work proposes control charts for double bounded and autocorrelated data based on the KARMA model. The results were numerically evaluated using Monte Carlo simulations, by analyzing the average run length (ARL) of the series under control (ARL0) and out of control (ARL1). The performance of the proposed control charts were compared with other methodologies in the literature, under different scenarios. The KARMA control charts outperforms the competitors in several scenarios, presenting the smallest distortions for ARL0 and the best power detection rates under out of control conditions. In a second part of this work, a robust methodology using weighted maximum likelihood estimators is proposed aiming to minimize the effect of outliers, which are typically present in historical hydrological series, in the performance of control charts. The estimators were evaluated with Monte Carlo simulations in terms of specific robustness measures and by comparing the performance of the control charts initially proposed versus the robust approach. It was identified that the robust control charts present better performance in the presence of outliers. Finally, the developed techniques are employed in real monitoring data of hydrological systems and the results are discussed. These control charts proved to be a useful tool for managing water storage, such as the Cantareira System and the reservoir of the Furnas hydroelectric power plant. In 2014, a crisis in the water supply of these systems was reported and the proposed charts were able to identify it. In this way, it is confirmed the potential of the proposed control charts to monitor water reservoir levels. |
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Gráficos de controle para variáveis ambientais duplamente limitadas e autocorrelacionadasControl charts for double bounded and autocorrelated environmental dataGráficos de controleHidrelétrica de FurnasModelo KARMAReservatórios de águaSistema CantareiraVerossimilhança ponderadaControl chartsFurnas hydropower plantKARMA modelOutliersWater reservoirCantareira systemWeighted likelihoodCNPQ::ENGENHARIAS::ENGENHARIA CIVILIn the environmental field, especially in hydrology, several activities require monitoring tools to assist in the process of decision making. Control chart is a statistical process control (SPC) tool that can be used for this purpose. However, one of the assumptions made for its use is the independence between different observations. In some processes, this assumption could not be verified, as in hydrological time series, which reduces the applicability of the usual control charts. A solution for this can be given by monitoring the residuals of a fitted time series model, such as, the Kumaraswamy autoregressive moving averages (KARMA) model, which was recently proposed for modeling double bounded environmental time series. In this context, this work proposes control charts for double bounded and autocorrelated data based on the KARMA model. The results were numerically evaluated using Monte Carlo simulations, by analyzing the average run length (ARL) of the series under control (ARL0) and out of control (ARL1). The performance of the proposed control charts were compared with other methodologies in the literature, under different scenarios. The KARMA control charts outperforms the competitors in several scenarios, presenting the smallest distortions for ARL0 and the best power detection rates under out of control conditions. In a second part of this work, a robust methodology using weighted maximum likelihood estimators is proposed aiming to minimize the effect of outliers, which are typically present in historical hydrological series, in the performance of control charts. The estimators were evaluated with Monte Carlo simulations in terms of specific robustness measures and by comparing the performance of the control charts initially proposed versus the robust approach. It was identified that the robust control charts present better performance in the presence of outliers. Finally, the developed techniques are employed in real monitoring data of hydrological systems and the results are discussed. These control charts proved to be a useful tool for managing water storage, such as the Cantareira System and the reservoir of the Furnas hydroelectric power plant. In 2014, a crisis in the water supply of these systems was reported and the proposed charts were able to identify it. In this way, it is confirmed the potential of the proposed control charts to monitor water reservoir levels.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESNa área ambiental, em especial na hidrológica, diversas atividades necessitam de ferramentas de monitoramento de variáveis que auxiliem nas tomadas de decisões. Os gráficos de controle são uma ferramenta do controle estatístico de processos (CEP) que podem ser utilizados para esse fim. No entanto, um dos pressupostos assumidos para seu uso é a independência entre as observações. Em alguns processos esse pressuposto pode não se verificar, como é o caso de sé- ries temporais hidrológicas, prejudicando o desempenho dos gráficos de controle usuais. Uma forma de contornar essa situação pode ser dada por meio do monitoramento dos resíduos de um modelo de séries temporais ajustado aos dados de interesse, como, por exemplo, o modelo Kumaraswamy autorregressivo de médias móveis (KARMA), o qual foi proposto recentemente para modelagem de séries temporais ambientais duplamente limitadas. Nesse contexto, este trabalho propõe gráficos de controle para variáveis duplamente limitadas e autocorrelacionadas baseados no modelo KARMA. Os resultados foram avaliados numericamente por simulações de Monte Carlo, analisando o comprimento médio de corrida (average run length - ARL) da série sob controle (ARL0) e fora de controle (ARL1). Comparou-se os desempenhos dos grá- ficos propostos com outras metodologias da literatura, sob diferentes cenários. Os gráficos de controle KARMA se destacaram em quase todos os cenários, apresentando as menores distor- ções de ARL0 e uma taxa de acerto superior ou próxima a dos outros modelos avaliados sob condições fora de controle. Em um segundo momento, é proposta uma metodologia robusta para minimizar o efeito da presença de outliers, comuns em séries históricas hidrológicas, no desempenho dos gráficos de controle, utilizando estimadores de máxima verossimilhança ponderada. Via simulação de Monte Carlo, os estimadores foram avaliados em termos de medidas específicas de robustez e comparado o desempenho dos gráficos de controle inicialmente propostos e a abordagem robusta. Identificou-se que os gráficos de controle robustos apresentam melhor desempenho quando na presença de outliers. Por fim, as técnincas desenvolvidas são utilizadas no monitoramento de séries hidrológicas reais e seus resultados são discutidos. Os gráficos de controle se mostraram uma ferramenta útil para o gerenciamento de sistemas de armazenamento de água, como é o caso do Sistema Cantareira e do reservatório da hidrelétrica de Furnas. Em 2014 foi noticiada uma crise no fornecimento de água nestes sistemas, e os gráficos propostos foram capazes de identificá-la. Desta forma, verifica-se a potencialidade da utilização dos gráficos propostos no monitoramento de níveis em reservatórios de água.Universidade Federal de Santa MariaBrasilEngenharia CivilUFSMPrograma de Pós-Graduação em Engenharia CivilCentro de TecnologiaBayer, Debora Missiohttp://lattes.cnpq.br/5799733583668443Bayer, Fabio Marianohttp://lattes.cnpq.br/9904863693302949Piccilli, Daniel Gustavo Allasiahttp://lattes.cnpq.br/3858010328968944Muller, Fernanda Mariahttp://lattes.cnpq.br/3226110093009785Conte, Luiza Chiarelli2021-09-15T03:30:18Z2021-09-15T03:30:18Z2020-02-28info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://repositorio.ufsm.br/handle/1/22218porAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessreponame:Manancial - Repositório Digital da UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM2022-06-24T14:09:33Zoai:repositorio.ufsm.br:1/22218Biblioteca Digital de Teses e Dissertaçõeshttps://repositorio.ufsm.br/ONGhttps://repositorio.ufsm.br/oai/requestatendimento.sib@ufsm.br||tedebc@gmail.comopendoar:2022-06-24T14:09:33Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM)false |
dc.title.none.fl_str_mv |
Gráficos de controle para variáveis ambientais duplamente limitadas e autocorrelacionadas Control charts for double bounded and autocorrelated environmental data |
title |
Gráficos de controle para variáveis ambientais duplamente limitadas e autocorrelacionadas |
spellingShingle |
Gráficos de controle para variáveis ambientais duplamente limitadas e autocorrelacionadas Conte, Luiza Chiarelli Gráficos de controle Hidrelétrica de Furnas Modelo KARMA Reservatórios de água Sistema Cantareira Verossimilhança ponderada Control charts Furnas hydropower plant KARMA model Outliers Water reservoir Cantareira system Weighted likelihood CNPQ::ENGENHARIAS::ENGENHARIA CIVIL |
title_short |
Gráficos de controle para variáveis ambientais duplamente limitadas e autocorrelacionadas |
title_full |
Gráficos de controle para variáveis ambientais duplamente limitadas e autocorrelacionadas |
title_fullStr |
Gráficos de controle para variáveis ambientais duplamente limitadas e autocorrelacionadas |
title_full_unstemmed |
Gráficos de controle para variáveis ambientais duplamente limitadas e autocorrelacionadas |
title_sort |
Gráficos de controle para variáveis ambientais duplamente limitadas e autocorrelacionadas |
author |
Conte, Luiza Chiarelli |
author_facet |
Conte, Luiza Chiarelli |
author_role |
author |
dc.contributor.none.fl_str_mv |
Bayer, Debora Missio http://lattes.cnpq.br/5799733583668443 Bayer, Fabio Mariano http://lattes.cnpq.br/9904863693302949 Piccilli, Daniel Gustavo Allasia http://lattes.cnpq.br/3858010328968944 Muller, Fernanda Maria http://lattes.cnpq.br/3226110093009785 |
dc.contributor.author.fl_str_mv |
Conte, Luiza Chiarelli |
dc.subject.por.fl_str_mv |
Gráficos de controle Hidrelétrica de Furnas Modelo KARMA Reservatórios de água Sistema Cantareira Verossimilhança ponderada Control charts Furnas hydropower plant KARMA model Outliers Water reservoir Cantareira system Weighted likelihood CNPQ::ENGENHARIAS::ENGENHARIA CIVIL |
topic |
Gráficos de controle Hidrelétrica de Furnas Modelo KARMA Reservatórios de água Sistema Cantareira Verossimilhança ponderada Control charts Furnas hydropower plant KARMA model Outliers Water reservoir Cantareira system Weighted likelihood CNPQ::ENGENHARIAS::ENGENHARIA CIVIL |
description |
In the environmental field, especially in hydrology, several activities require monitoring tools to assist in the process of decision making. Control chart is a statistical process control (SPC) tool that can be used for this purpose. However, one of the assumptions made for its use is the independence between different observations. In some processes, this assumption could not be verified, as in hydrological time series, which reduces the applicability of the usual control charts. A solution for this can be given by monitoring the residuals of a fitted time series model, such as, the Kumaraswamy autoregressive moving averages (KARMA) model, which was recently proposed for modeling double bounded environmental time series. In this context, this work proposes control charts for double bounded and autocorrelated data based on the KARMA model. The results were numerically evaluated using Monte Carlo simulations, by analyzing the average run length (ARL) of the series under control (ARL0) and out of control (ARL1). The performance of the proposed control charts were compared with other methodologies in the literature, under different scenarios. The KARMA control charts outperforms the competitors in several scenarios, presenting the smallest distortions for ARL0 and the best power detection rates under out of control conditions. In a second part of this work, a robust methodology using weighted maximum likelihood estimators is proposed aiming to minimize the effect of outliers, which are typically present in historical hydrological series, in the performance of control charts. The estimators were evaluated with Monte Carlo simulations in terms of specific robustness measures and by comparing the performance of the control charts initially proposed versus the robust approach. It was identified that the robust control charts present better performance in the presence of outliers. Finally, the developed techniques are employed in real monitoring data of hydrological systems and the results are discussed. These control charts proved to be a useful tool for managing water storage, such as the Cantareira System and the reservoir of the Furnas hydroelectric power plant. In 2014, a crisis in the water supply of these systems was reported and the proposed charts were able to identify it. In this way, it is confirmed the potential of the proposed control charts to monitor water reservoir levels. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-02-28 2021-09-15T03:30:18Z 2021-09-15T03:30:18Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.ufsm.br/handle/1/22218 |
url |
http://repositorio.ufsm.br/handle/1/22218 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Brasil Engenharia Civil UFSM Programa de Pós-Graduação em Engenharia Civil Centro de Tecnologia |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Brasil Engenharia Civil UFSM Programa de Pós-Graduação em Engenharia Civil Centro de Tecnologia |
dc.source.none.fl_str_mv |
reponame:Manancial - Repositório Digital da UFSM instname:Universidade Federal de Santa Maria (UFSM) instacron:UFSM |
instname_str |
Universidade Federal de Santa Maria (UFSM) |
instacron_str |
UFSM |
institution |
UFSM |
reponame_str |
Manancial - Repositório Digital da UFSM |
collection |
Manancial - Repositório Digital da UFSM |
repository.name.fl_str_mv |
Manancial - Repositório Digital da UFSM - Universidade Federal de Santa Maria (UFSM) |
repository.mail.fl_str_mv |
atendimento.sib@ufsm.br||tedebc@gmail.com |
_version_ |
1805922023139966976 |