Tratamento de grandes volumes de dados hidrometeorol?gicos apoiados por valida??o cruzada em Workflows cient?ficos
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| Data de Publicação: | 2016 |
| Tipo de documento: | Dissertação |
| Idioma: | por |
| Título da fonte: | Biblioteca Digital de Teses e Dissertações da UFRRJ |
| Texto Completo: | https://tede.ufrrj.br/jspui/handle/jspui/2592 |
Resumo: | Researchers face several challenges in order to handle large volumes of high-quality meteorological data, free of missing (or gaps). The detailed comprehension of rainfall temporal data is important in daily activities such as in the evaluation of the distribution of rain in a given area. The proper study of such dataset may aid administrators in planning cities, industries and farmlands. In Brazil, meteorological dataset is collect by meteorological rainfall stations that are spread in the geographic space. Part of such dataset are maintained Ag?ncia Nacional de ?guas (ANA) by means of HidroWeb system. Activities like detect and fill temporal series gaps are crucial to control the quality of meteorological data. This dissertation presents a computational approach based on scientific workflows and crossvalidation and interpolation methods to face the above-mentioned challenges. The interpolation methods we have used in this dissertation are linear regression,regional weighting, inverse square distance, regional weighting with linear regression. The scientific workflows we have developed are able to use the four methods to generate large volumes of high meteorological data; they are also able to collect retrospective provenance from the in silico experiments. Our experiments evaluated dataset from 34 (from 77) meteorological rainfall stations which the temporal serial has at least ten years of data. Our experimental results showed that the best results were achieved with PR method |
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Cruz, S?rgio Manuel Serra da848.488.637-91http://lattes.cnpq.br/7618571401128973Greg?rio, Ronaldo Malheiros077.117.167-61http://lattes.cnpq.br/4502104424266743Santos, Ednaldo Oliveira dosManh?es, Laci Mari Barbosa019.285.167-50http://lattes.cnpq.br/3015449888051774Tomaz, Ulisses Roque2019-02-26T15:32:13Z2016-09-19TOMAZ, Ulisses Roque. Tratamento de grandes volumes de dados hidrometeorol?gicos apoiados por valida??o cruzada em Workflows cient?ficos. 2016. 117 f. Disserta??o (Mestrado em Modelagem Matem?tica e Computacional) - Instituto de Ci?ncias Exatas, Universidade Federal Rural do Rio de Janeiro, Serop?dica - RJ, 2016.https://tede.ufrrj.br/jspui/handle/jspui/2592Researchers face several challenges in order to handle large volumes of high-quality meteorological data, free of missing (or gaps). The detailed comprehension of rainfall temporal data is important in daily activities such as in the evaluation of the distribution of rain in a given area. The proper study of such dataset may aid administrators in planning cities, industries and farmlands. In Brazil, meteorological dataset is collect by meteorological rainfall stations that are spread in the geographic space. Part of such dataset are maintained Ag?ncia Nacional de ?guas (ANA) by means of HidroWeb system. Activities like detect and fill temporal series gaps are crucial to control the quality of meteorological data. This dissertation presents a computational approach based on scientific workflows and crossvalidation and interpolation methods to face the above-mentioned challenges. The interpolation methods we have used in this dissertation are linear regression,regional weighting, inverse square distance, regional weighting with linear regression. The scientific workflows we have developed are able to use the four methods to generate large volumes of high meteorological data; they are also able to collect retrospective provenance from the in silico experiments. Our experiments evaluated dataset from 34 (from 77) meteorological rainfall stations which the temporal serial has at least ten years of data. Our experimental results showed that the best results were achieved with PR methodObtergrandes volumes de dados meteorol?gicos de qualidade e livre de falhas s?o grandes desafios para estudos clim?ticos ambientais.O estudo de dados pluviom?tricos ? de grande import?ncia no dia a dia, pois viabiliza o entendimento da variabilidade espacial da precipita??o em uma regi?o ou bacia hidrogr?fica o que possibilita determinar o per?odo e avaliar a probabilidade da ocorr?ncia de eventos extremos, sendo de suma import?ncia para as a??es associadas ao planejamento urbano, industrial e agropecu?rio, al?m do uso racional dos recursos h?dricos.No Brasil, eles s?o obtidos a partir de esta??es meteorol?gicas, geograficamente distribu?dasem todo territ?rio e fornecidos por v?rios ?rg?os, principalmente, peloInstituto Nacional de Meteorologia (INMET)e pela Ag?ncia Nacional de ?guas (ANA).No entanto, obter dados estruturados curados de qualidade e livre de falhas ? um problema que ainda ? estudado por diversos autores. Assim, detectar e preencher as falhas encontradas nos dados ? um passo importante para o controle de qualidade. Neste sentido, este trabalho estuda e aplica o m?todo de valida??o cruzada para a sele??o dos m?todos de interpola??o (regress?o linear, pondera??o regional, inverso do quadrado da dist?ncia e pondera??o regional com base em regress?es lineares) no preenchimento de falhasde longas s?ries de dados pelo uso em workflows cient?ficos. Para controlar, integrar e produzir essa massa de dados curados, as tarefas de computa??o se apoiaram na execu??o de experimentos cient?ficosin silico voltados para a ?rea da Meteorologia baseadas no paradigma dos workflows cient?ficos, que capturaram descritores de proveni?ncia, que auxiliam na rastreabilidade dos dados e processos, e assim, revelam como foram produzidos, e, ainda, asseguram a qualidade da metodologia aplicada. Esta pesquisa prop?s, modelou e avaliou umworkflow cient?fico com base em experimentos computacionais capazes de manipular grandes volumes de dados meteorol?gicos brutos, transformando-os em curados e estabelecendo sua proveni?ncia. Al?m disso, a proposta consiste em armazen?-los na base de dados compat?vel com o sistema Meteoro desenvolvido previamente pelo nosso grupo de pesquisas.Neste processo foram analisados os dados hidrol?gicos de 34 esta??es pluviom?tricas (s?ries com no m?nimo 10 anos), de 77 inicialmente selecionadas e,dentre os m?todos avaliados o que apresentou melhores resultados foi o da pondera??o regional (PR).Submitted by Celso Magalhaes (celsomagalhaes@ufrrj.br) on 2019-02-26T15:32:13Z No. of bitstreams: 1 2016 - Ulisses Roque Tomaz.pdf: 20967061 bytes, checksum: 1f4def6f15f5b8861307d659a73e7bff (MD5)Made available in DSpace on 2019-02-26T15:32:13Z (GMT). No. of bitstreams: 1 2016 - Ulisses Roque Tomaz.pdf: 20967061 bytes, checksum: 1f4def6f15f5b8861307d659a73e7bff (MD5) Previous issue date: 2016-09-19application/pdfhttps://tede.ufrrj.br/retrieve/9320/2016%20-%20Ulisses%20Roque%20Tomaz.pdf.jpghttps://tede.ufrrj.br/retrieve/15780/2016%20-%20Ulisses%20Roque%20Tomaz.pdf.jpghttps://tede.ufrrj.br/retrieve/22092/2016%20-%20Ulisses%20Roque%20Tomaz.pdf.jpghttps://tede.ufrrj.br/retrieve/28476/2016%20-%20Ulisses%20Roque%20Tomaz.pdf.jpghttps://tede.ufrrj.br/retrieve/34892/2016%20-%20Ulisses%20Roque%20Tomaz.pdf.jpghttps://tede.ufrrj.br/retrieve/41288/2016%20-%20Ulisses%20Roque%20Tomaz.pdf.jpghttps://tede.ufrrj.br/retrieve/47690/2016%20-%20Ulisses%20Roque%20Tomaz.pdf.jpghttps://tede.ufrrj.br/retrieve/54126/2016%20-%20Ulisses%20Roque%20Tomaz.pdf.jpgporUniversidade Federal Rural do Rio de JaneiroPrograma de P?s-Gradua??o em Modelagem Matem?tica e ComputacionalUFRRJBrasilInstituto de Ci?ncias Exatas(COOPERATIVE INFORMATION SYSTEMS). 1. ed. 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| dc.title.por.fl_str_mv |
Tratamento de grandes volumes de dados hidrometeorol?gicos apoiados por valida??o cruzada em Workflows cient?ficos |
| dc.title.alternative.eng.fl_str_mv |
Treatment of large volumes of hydrometeorological data supported by cross-validation in scientific workflows |
| title |
Tratamento de grandes volumes de dados hidrometeorol?gicos apoiados por valida??o cruzada em Workflows cient?ficos |
| spellingShingle |
Tratamento de grandes volumes de dados hidrometeorol?gicos apoiados por valida??o cruzada em Workflows cient?ficos Tomaz, Ulisses Roque Workflow cient?fico valida??o cruzada proveni?ncia Scientific workflow cross-validation provenance Matem?tica |
| title_short |
Tratamento de grandes volumes de dados hidrometeorol?gicos apoiados por valida??o cruzada em Workflows cient?ficos |
| title_full |
Tratamento de grandes volumes de dados hidrometeorol?gicos apoiados por valida??o cruzada em Workflows cient?ficos |
| title_fullStr |
Tratamento de grandes volumes de dados hidrometeorol?gicos apoiados por valida??o cruzada em Workflows cient?ficos |
| title_full_unstemmed |
Tratamento de grandes volumes de dados hidrometeorol?gicos apoiados por valida??o cruzada em Workflows cient?ficos |
| title_sort |
Tratamento de grandes volumes de dados hidrometeorol?gicos apoiados por valida??o cruzada em Workflows cient?ficos |
| author |
Tomaz, Ulisses Roque |
| author_facet |
Tomaz, Ulisses Roque |
| author_role |
author |
| dc.contributor.advisor1.fl_str_mv |
Cruz, S?rgio Manuel Serra da |
| dc.contributor.advisor1ID.fl_str_mv |
848.488.637-91 |
| dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/7618571401128973 |
| dc.contributor.advisor-co1.fl_str_mv |
Greg?rio, Ronaldo Malheiros |
| dc.contributor.advisor-co1ID.fl_str_mv |
077.117.167-61 |
| dc.contributor.advisor-co1Lattes.fl_str_mv |
http://lattes.cnpq.br/4502104424266743 |
| dc.contributor.referee1.fl_str_mv |
Santos, Ednaldo Oliveira dos |
| dc.contributor.referee2.fl_str_mv |
Manh?es, Laci Mari Barbosa |
| dc.contributor.authorID.fl_str_mv |
019.285.167-50 |
| dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/3015449888051774 |
| dc.contributor.author.fl_str_mv |
Tomaz, Ulisses Roque |
| contributor_str_mv |
Cruz, S?rgio Manuel Serra da Greg?rio, Ronaldo Malheiros Santos, Ednaldo Oliveira dos Manh?es, Laci Mari Barbosa |
| dc.subject.por.fl_str_mv |
Workflow cient?fico valida??o cruzada proveni?ncia |
| topic |
Workflow cient?fico valida??o cruzada proveni?ncia Scientific workflow cross-validation provenance Matem?tica |
| dc.subject.eng.fl_str_mv |
Scientific workflow cross-validation provenance |
| dc.subject.cnpq.fl_str_mv |
Matem?tica |
| description |
Researchers face several challenges in order to handle large volumes of high-quality meteorological data, free of missing (or gaps). The detailed comprehension of rainfall temporal data is important in daily activities such as in the evaluation of the distribution of rain in a given area. The proper study of such dataset may aid administrators in planning cities, industries and farmlands. In Brazil, meteorological dataset is collect by meteorological rainfall stations that are spread in the geographic space. Part of such dataset are maintained Ag?ncia Nacional de ?guas (ANA) by means of HidroWeb system. Activities like detect and fill temporal series gaps are crucial to control the quality of meteorological data. This dissertation presents a computational approach based on scientific workflows and crossvalidation and interpolation methods to face the above-mentioned challenges. The interpolation methods we have used in this dissertation are linear regression,regional weighting, inverse square distance, regional weighting with linear regression. The scientific workflows we have developed are able to use the four methods to generate large volumes of high meteorological data; they are also able to collect retrospective provenance from the in silico experiments. Our experiments evaluated dataset from 34 (from 77) meteorological rainfall stations which the temporal serial has at least ten years of data. Our experimental results showed that the best results were achieved with PR method |
| publishDate |
2016 |
| dc.date.issued.fl_str_mv |
2016-09-19 |
| dc.date.accessioned.fl_str_mv |
2019-02-26T15:32:13Z |
| 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.citation.fl_str_mv |
TOMAZ, Ulisses Roque. Tratamento de grandes volumes de dados hidrometeorol?gicos apoiados por valida??o cruzada em Workflows cient?ficos. 2016. 117 f. Disserta??o (Mestrado em Modelagem Matem?tica e Computacional) - Instituto de Ci?ncias Exatas, Universidade Federal Rural do Rio de Janeiro, Serop?dica - RJ, 2016. |
| dc.identifier.uri.fl_str_mv |
https://tede.ufrrj.br/jspui/handle/jspui/2592 |
| identifier_str_mv |
TOMAZ, Ulisses Roque. Tratamento de grandes volumes de dados hidrometeorol?gicos apoiados por valida??o cruzada em Workflows cient?ficos. 2016. 117 f. Disserta??o (Mestrado em Modelagem Matem?tica e Computacional) - Instituto de Ci?ncias Exatas, Universidade Federal Rural do Rio de Janeiro, Serop?dica - RJ, 2016. |
| url |
https://tede.ufrrj.br/jspui/handle/jspui/2592 |
| dc.language.iso.fl_str_mv |
por |
| language |
por |
| dc.relation.references.por.fl_str_mv |
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