Comparison of data mining models applied to a surface meteorological station
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | RBRH (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312017000100253 |
Resumo: | ABSTRACT This paper presents the application of data mining techniques for pattern identification obtained from the analysis of meteorological variables and their correlation with the occurrence of intense rainfall. The used data were collected between 2008 and 2012 by the surface meteorological station of the Polytechnic Institute of Rio de Janeiro State University, located in Nova Friburgo - RJ, Brazil. The main objective is the automatic prediction related to extreme precipitation events surrounding the meteorological station location one hour prior its occurrence. Classification models were developed based on decision trees and artificial neural networks. The steps of consistency analysis, treatment and data conversion, as well as the computational models used are described, and some metrics are compared in order to identify their effectiveness. The results obtained for the most accurate model presented a rate of 82. 9% of hits related to the prediction of rainfall equal to or greater than 10 mm h-1 one hour prior its occurrence. The results indicate the possibility of using this work to predict risk events in the study region. |
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Comparison of data mining models applied to a surface meteorological stationData miningClimate predictionSurface meteorological stationABSTRACT This paper presents the application of data mining techniques for pattern identification obtained from the analysis of meteorological variables and their correlation with the occurrence of intense rainfall. The used data were collected between 2008 and 2012 by the surface meteorological station of the Polytechnic Institute of Rio de Janeiro State University, located in Nova Friburgo - RJ, Brazil. The main objective is the automatic prediction related to extreme precipitation events surrounding the meteorological station location one hour prior its occurrence. Classification models were developed based on decision trees and artificial neural networks. The steps of consistency analysis, treatment and data conversion, as well as the computational models used are described, and some metrics are compared in order to identify their effectiveness. The results obtained for the most accurate model presented a rate of 82. 9% of hits related to the prediction of rainfall equal to or greater than 10 mm h-1 one hour prior its occurrence. The results indicate the possibility of using this work to predict risk events in the study region.Associação Brasileira de Recursos Hídricos2017-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312017000100253RBRH v.22 2017reponame:RBRH (Online)instname:Associação Brasileira de Recursos Hídricos (ABRH)instacron:ABRH10.1590/2318-0331.0217170029info:eu-repo/semantics/openAccessCharles,Anderson CordeiroNamen,Anderson AmendoeiraRodrigues,Pedro Paulo Gomes Wattseng2017-10-26T00:00:00Zoai:scielo:S2318-03312017000100253Revistahttps://www.scielo.br/j/rbrh/https://old.scielo.br/oai/scielo-oai.php||rbrh@abrh.org.br2318-03311414-381Xopendoar:2017-10-26T00:00RBRH (Online) - Associação Brasileira de Recursos Hídricos (ABRH)false |
dc.title.none.fl_str_mv |
Comparison of data mining models applied to a surface meteorological station |
title |
Comparison of data mining models applied to a surface meteorological station |
spellingShingle |
Comparison of data mining models applied to a surface meteorological station Charles,Anderson Cordeiro Data mining Climate prediction Surface meteorological station |
title_short |
Comparison of data mining models applied to a surface meteorological station |
title_full |
Comparison of data mining models applied to a surface meteorological station |
title_fullStr |
Comparison of data mining models applied to a surface meteorological station |
title_full_unstemmed |
Comparison of data mining models applied to a surface meteorological station |
title_sort |
Comparison of data mining models applied to a surface meteorological station |
author |
Charles,Anderson Cordeiro |
author_facet |
Charles,Anderson Cordeiro Namen,Anderson Amendoeira Rodrigues,Pedro Paulo Gomes Watts |
author_role |
author |
author2 |
Namen,Anderson Amendoeira Rodrigues,Pedro Paulo Gomes Watts |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Charles,Anderson Cordeiro Namen,Anderson Amendoeira Rodrigues,Pedro Paulo Gomes Watts |
dc.subject.por.fl_str_mv |
Data mining Climate prediction Surface meteorological station |
topic |
Data mining Climate prediction Surface meteorological station |
description |
ABSTRACT This paper presents the application of data mining techniques for pattern identification obtained from the analysis of meteorological variables and their correlation with the occurrence of intense rainfall. The used data were collected between 2008 and 2012 by the surface meteorological station of the Polytechnic Institute of Rio de Janeiro State University, located in Nova Friburgo - RJ, Brazil. The main objective is the automatic prediction related to extreme precipitation events surrounding the meteorological station location one hour prior its occurrence. Classification models were developed based on decision trees and artificial neural networks. The steps of consistency analysis, treatment and data conversion, as well as the computational models used are described, and some metrics are compared in order to identify their effectiveness. The results obtained for the most accurate model presented a rate of 82. 9% of hits related to the prediction of rainfall equal to or greater than 10 mm h-1 one hour prior its occurrence. The results indicate the possibility of using this work to predict risk events in the study region. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312017000100253 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312017000100253 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/2318-0331.0217170029 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Associação Brasileira de Recursos Hídricos |
publisher.none.fl_str_mv |
Associação Brasileira de Recursos Hídricos |
dc.source.none.fl_str_mv |
RBRH v.22 2017 reponame:RBRH (Online) instname:Associação Brasileira de Recursos Hídricos (ABRH) instacron:ABRH |
instname_str |
Associação Brasileira de Recursos Hídricos (ABRH) |
instacron_str |
ABRH |
institution |
ABRH |
reponame_str |
RBRH (Online) |
collection |
RBRH (Online) |
repository.name.fl_str_mv |
RBRH (Online) - Associação Brasileira de Recursos Hídricos (ABRH) |
repository.mail.fl_str_mv |
||rbrh@abrh.org.br |
_version_ |
1754734701490208768 |