Using clustering algorithms and GPM data to identify spatial precipitation patterns over southeastern Brazil
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.20937/ATM.53155 http://hdl.handle.net/11449/247251 |
Resumo: | Southeastern Brazil comprises an important geoeconomic and populous region in South America. Consequently, it is essential to analyze and understand the precipitation profiles in this region. Among different data sources and techniques available to perform such study, the use of clustering algorithms and information from the Global Precipitation Measurement (GPM) project emerges as a convenient, yet less exploited alternative. This study employs the K-Means, the Hierarchical Ward, and the Self-Organizing Maps methods to cluster the annual and seasonal precipitation data from GPM project recorded from 2001 to 2019. The adopted methods are compared in terms of quantitative measures and the number of clusters defined through a well-established rule. The results demonstrate that the annual and seasonal periods are organized according to different number of clusters. Moreover, the results allow: identify the presence of a spatially heterogeneous distribution in the study area; to conclude that the K-Means algorithm is a suitable clustering method in the context of this investigation when compared to Ward’s Hierarchical and Self-Organizing Maps methods in terms of the Calinski-Harabasz and Davies-Bouldin measures; and that the spatial precipitation distribution over Southeastern Brazil is represented by 10 clusters in annual and summer periods, 11 clusters in autumn and spring and 9 clusters in winter period |
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Using clustering algorithms and GPM data to identify spatial precipitation patterns over southeastern Brazilclustering algorithmsGPMprecipitationsoutheastern BrazilSoutheastern Brazil comprises an important geoeconomic and populous region in South America. Consequently, it is essential to analyze and understand the precipitation profiles in this region. Among different data sources and techniques available to perform such study, the use of clustering algorithms and information from the Global Precipitation Measurement (GPM) project emerges as a convenient, yet less exploited alternative. This study employs the K-Means, the Hierarchical Ward, and the Self-Organizing Maps methods to cluster the annual and seasonal precipitation data from GPM project recorded from 2001 to 2019. The adopted methods are compared in terms of quantitative measures and the number of clusters defined through a well-established rule. The results demonstrate that the annual and seasonal periods are organized according to different number of clusters. Moreover, the results allow: identify the presence of a spatially heterogeneous distribution in the study area; to conclude that the K-Means algorithm is a suitable clustering method in the context of this investigation when compared to Ward’s Hierarchical and Self-Organizing Maps methods in terms of the Calinski-Harabasz and Davies-Bouldin measures; and that the spatial precipitation distribution over Southeastern Brazil is represented by 10 clusters in annual and summer periods, 11 clusters in autumn and spring and 9 clusters in winter periodConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)São Paulo State University (UNESP) Institute of Science and Technology, São PauloGraduate Program in Natural Disasters São Paulo State University (UNESP) National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), São PauloSão Paulo State University (UNESP) Institute of Science and Technology, São PauloGraduate Program in Natural Disasters São Paulo State University (UNESP) National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), São PauloCNPq: 426530/20187Universidade Estadual Paulista (UNESP)Miranda, Bruno Guerreiro [UNESP]Negri, Rogério Galante [UNESP]Pampuch, Luana Albertani [UNESP]2023-07-29T13:10:51Z2023-07-29T13:10:51Z2023-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article365-381http://dx.doi.org/10.20937/ATM.53155Atmosfera, v. 37, p. 365-381.2395-88120187-6236http://hdl.handle.net/11449/24725110.20937/ATM.531552-s2.0-85153774161Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengAtmosferainfo:eu-repo/semantics/openAccess2023-07-29T13:10:51Zoai:repositorio.unesp.br:11449/247251Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:31:05.620780Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Using clustering algorithms and GPM data to identify spatial precipitation patterns over southeastern Brazil |
title |
Using clustering algorithms and GPM data to identify spatial precipitation patterns over southeastern Brazil |
spellingShingle |
Using clustering algorithms and GPM data to identify spatial precipitation patterns over southeastern Brazil Miranda, Bruno Guerreiro [UNESP] clustering algorithms GPM precipitation southeastern Brazil |
title_short |
Using clustering algorithms and GPM data to identify spatial precipitation patterns over southeastern Brazil |
title_full |
Using clustering algorithms and GPM data to identify spatial precipitation patterns over southeastern Brazil |
title_fullStr |
Using clustering algorithms and GPM data to identify spatial precipitation patterns over southeastern Brazil |
title_full_unstemmed |
Using clustering algorithms and GPM data to identify spatial precipitation patterns over southeastern Brazil |
title_sort |
Using clustering algorithms and GPM data to identify spatial precipitation patterns over southeastern Brazil |
author |
Miranda, Bruno Guerreiro [UNESP] |
author_facet |
Miranda, Bruno Guerreiro [UNESP] Negri, Rogério Galante [UNESP] Pampuch, Luana Albertani [UNESP] |
author_role |
author |
author2 |
Negri, Rogério Galante [UNESP] Pampuch, Luana Albertani [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Miranda, Bruno Guerreiro [UNESP] Negri, Rogério Galante [UNESP] Pampuch, Luana Albertani [UNESP] |
dc.subject.por.fl_str_mv |
clustering algorithms GPM precipitation southeastern Brazil |
topic |
clustering algorithms GPM precipitation southeastern Brazil |
description |
Southeastern Brazil comprises an important geoeconomic and populous region in South America. Consequently, it is essential to analyze and understand the precipitation profiles in this region. Among different data sources and techniques available to perform such study, the use of clustering algorithms and information from the Global Precipitation Measurement (GPM) project emerges as a convenient, yet less exploited alternative. This study employs the K-Means, the Hierarchical Ward, and the Self-Organizing Maps methods to cluster the annual and seasonal precipitation data from GPM project recorded from 2001 to 2019. The adopted methods are compared in terms of quantitative measures and the number of clusters defined through a well-established rule. The results demonstrate that the annual and seasonal periods are organized according to different number of clusters. Moreover, the results allow: identify the presence of a spatially heterogeneous distribution in the study area; to conclude that the K-Means algorithm is a suitable clustering method in the context of this investigation when compared to Ward’s Hierarchical and Self-Organizing Maps methods in terms of the Calinski-Harabasz and Davies-Bouldin measures; and that the spatial precipitation distribution over Southeastern Brazil is represented by 10 clusters in annual and summer periods, 11 clusters in autumn and spring and 9 clusters in winter period |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-29T13:10:51Z 2023-07-29T13:10:51Z 2023-01-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.20937/ATM.53155 Atmosfera, v. 37, p. 365-381. 2395-8812 0187-6236 http://hdl.handle.net/11449/247251 10.20937/ATM.53155 2-s2.0-85153774161 |
url |
http://dx.doi.org/10.20937/ATM.53155 http://hdl.handle.net/11449/247251 |
identifier_str_mv |
Atmosfera, v. 37, p. 365-381. 2395-8812 0187-6236 10.20937/ATM.53155 2-s2.0-85153774161 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Atmosfera |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
365-381 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128940424822784 |