Climate Regionalization in Mato Grosso do Sul: a Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperature
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Archives of Biology and Technology |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132022000100704 |
Resumo: | Abstract The climatic zones of Mato Grosso do Sul (MS) were defined based on the mathematical methodology of cluster analysis (CA). Data from 77 climatic seasons of average annual temperatures (maximum and minimum) and total annual precipitation data from 1978 to 2013 were used, and hierarchical (Ward) and partitional or non-hierarchical (k-means) CA algorithms were chosen, as two of the most used approaches, to carry out the regionalization. The optimum number of clusters in which the data can be grouped was determined by the statistical methods of elbow, silhouette and gap. The stability of the clusters is also tested by statistical approaches and four homogeneous groups were found, as in conventional climatic zones, but with considerable border differences. Pearson's correlation coefficient (r) between the series in each cluster helps to understand the dynamics of these clusters. The hierarchical cluster analysis and the elbow method for the optimal number of clusters was the most appropriate and satisfactory and was able to train and validate homogeneous regions of climate in the state of Mato Grosso do Sul. The efficient application of these methodologies is confirmed by the delimitation of four distinct clusters (homogeneous regions of climate), consistent with recorded heights and temperatures (maximum and minimum) and geographical characteristics as topography, in the state of Mato Grosso do Sul. |
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Brazilian Archives of Biology and Technology |
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Climate Regionalization in Mato Grosso do Sul: a Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperaturecluster analysisclimatic zonesclimate regionalizationMato Grosso do SulAbstract The climatic zones of Mato Grosso do Sul (MS) were defined based on the mathematical methodology of cluster analysis (CA). Data from 77 climatic seasons of average annual temperatures (maximum and minimum) and total annual precipitation data from 1978 to 2013 were used, and hierarchical (Ward) and partitional or non-hierarchical (k-means) CA algorithms were chosen, as two of the most used approaches, to carry out the regionalization. The optimum number of clusters in which the data can be grouped was determined by the statistical methods of elbow, silhouette and gap. The stability of the clusters is also tested by statistical approaches and four homogeneous groups were found, as in conventional climatic zones, but with considerable border differences. Pearson's correlation coefficient (r) between the series in each cluster helps to understand the dynamics of these clusters. The hierarchical cluster analysis and the elbow method for the optimal number of clusters was the most appropriate and satisfactory and was able to train and validate homogeneous regions of climate in the state of Mato Grosso do Sul. The efficient application of these methodologies is confirmed by the delimitation of four distinct clusters (homogeneous regions of climate), consistent with recorded heights and temperatures (maximum and minimum) and geographical characteristics as topography, in the state of Mato Grosso do Sul.Instituto de Tecnologia do Paraná - Tecpar2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132022000100704Brazilian Archives of Biology and Technology v.65 2022reponame:Brazilian Archives of Biology and Technologyinstname:Instituto de Tecnologia do Paraná (Tecpar)instacron:TECPAR10.1590/1678-4324-2022210331info:eu-repo/semantics/openAccessSouza,Amaury deAbreu,Marcel Carvalhode Oliveira-Júnior,José FranciscoAristone,FlavioFernandes,Widinei AlvesAviv-Sharon,ElinorGraf,Renataeng2022-05-12T00:00:00Zoai:scielo:S1516-89132022000100704Revistahttps://www.scielo.br/j/babt/https://old.scielo.br/oai/scielo-oai.phpbabt@tecpar.br||babt@tecpar.br1678-43241516-8913opendoar:2022-05-12T00:00Brazilian Archives of Biology and Technology - Instituto de Tecnologia do Paraná (Tecpar)false |
dc.title.none.fl_str_mv |
Climate Regionalization in Mato Grosso do Sul: a Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperature |
title |
Climate Regionalization in Mato Grosso do Sul: a Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperature |
spellingShingle |
Climate Regionalization in Mato Grosso do Sul: a Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperature Souza,Amaury de cluster analysis climatic zones climate regionalization Mato Grosso do Sul |
title_short |
Climate Regionalization in Mato Grosso do Sul: a Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperature |
title_full |
Climate Regionalization in Mato Grosso do Sul: a Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperature |
title_fullStr |
Climate Regionalization in Mato Grosso do Sul: a Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperature |
title_full_unstemmed |
Climate Regionalization in Mato Grosso do Sul: a Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperature |
title_sort |
Climate Regionalization in Mato Grosso do Sul: a Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperature |
author |
Souza,Amaury de |
author_facet |
Souza,Amaury de Abreu,Marcel Carvalho de Oliveira-Júnior,José Francisco Aristone,Flavio Fernandes,Widinei Alves Aviv-Sharon,Elinor Graf,Renata |
author_role |
author |
author2 |
Abreu,Marcel Carvalho de Oliveira-Júnior,José Francisco Aristone,Flavio Fernandes,Widinei Alves Aviv-Sharon,Elinor Graf,Renata |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Souza,Amaury de Abreu,Marcel Carvalho de Oliveira-Júnior,José Francisco Aristone,Flavio Fernandes,Widinei Alves Aviv-Sharon,Elinor Graf,Renata |
dc.subject.por.fl_str_mv |
cluster analysis climatic zones climate regionalization Mato Grosso do Sul |
topic |
cluster analysis climatic zones climate regionalization Mato Grosso do Sul |
description |
Abstract The climatic zones of Mato Grosso do Sul (MS) were defined based on the mathematical methodology of cluster analysis (CA). Data from 77 climatic seasons of average annual temperatures (maximum and minimum) and total annual precipitation data from 1978 to 2013 were used, and hierarchical (Ward) and partitional or non-hierarchical (k-means) CA algorithms were chosen, as two of the most used approaches, to carry out the regionalization. The optimum number of clusters in which the data can be grouped was determined by the statistical methods of elbow, silhouette and gap. The stability of the clusters is also tested by statistical approaches and four homogeneous groups were found, as in conventional climatic zones, but with considerable border differences. Pearson's correlation coefficient (r) between the series in each cluster helps to understand the dynamics of these clusters. The hierarchical cluster analysis and the elbow method for the optimal number of clusters was the most appropriate and satisfactory and was able to train and validate homogeneous regions of climate in the state of Mato Grosso do Sul. The efficient application of these methodologies is confirmed by the delimitation of four distinct clusters (homogeneous regions of climate), consistent with recorded heights and temperatures (maximum and minimum) and geographical characteristics as topography, in the state of Mato Grosso do Sul. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 |
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=S1516-89132022000100704 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132022000100704 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1678-4324-2022210331 |
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 |
Instituto de Tecnologia do Paraná - Tecpar |
publisher.none.fl_str_mv |
Instituto de Tecnologia do Paraná - Tecpar |
dc.source.none.fl_str_mv |
Brazilian Archives of Biology and Technology v.65 2022 reponame:Brazilian Archives of Biology and Technology instname:Instituto de Tecnologia do Paraná (Tecpar) instacron:TECPAR |
instname_str |
Instituto de Tecnologia do Paraná (Tecpar) |
instacron_str |
TECPAR |
institution |
TECPAR |
reponame_str |
Brazilian Archives of Biology and Technology |
collection |
Brazilian Archives of Biology and Technology |
repository.name.fl_str_mv |
Brazilian Archives of Biology and Technology - Instituto de Tecnologia do Paraná (Tecpar) |
repository.mail.fl_str_mv |
babt@tecpar.br||babt@tecpar.br |
_version_ |
1750318281710895104 |