Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networks

Detalhes bibliográficos
Autor(a) principal: CECíLIO,ROBERTO A.
Data de Publicação: 2013
Outros Autores: MOREIRA,MICHEL C., PEZZOPANE,JOSé EDUARDO M., PRUSKI,FERNANDO F., FUKUNAGA,DANILO C.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Anais da Academia Brasileira de Ciências (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652013000401523
Resumo: The rainfall parameter that expresses the capacity to promote soil erosion is called rainfall erosivity (R), and is commonly represented by the indexes EI30 and KE>25. The calculations of these indexes requires pluviographical records, that are difficult to obtain in Brazil. This paper describes the use of synthetic rainfall series to compute EI30 and KE>25 in Espírito Santo State (Brazil). Artificial neural networks (ANNs) were also developed to spatially interpolate R values in Espírito Santo. EI30 and KE>25 indexes values were close to those calculated on a homogeneous area according to the similarity of rainfall distribution; indicating the applicability of the use of synthetic rainfall series to estimate the R factor. ANNs had a better performance than Inverse Distance Weighted and Kriging to spatially interpolate rainfall erosivity values in the State of Espírito Santo.
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spelling Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networksinterpolationrainfall generatorsoil conservationuniversal soil loss equationThe rainfall parameter that expresses the capacity to promote soil erosion is called rainfall erosivity (R), and is commonly represented by the indexes EI30 and KE>25. The calculations of these indexes requires pluviographical records, that are difficult to obtain in Brazil. This paper describes the use of synthetic rainfall series to compute EI30 and KE>25 in Espírito Santo State (Brazil). Artificial neural networks (ANNs) were also developed to spatially interpolate R values in Espírito Santo. EI30 and KE>25 indexes values were close to those calculated on a homogeneous area according to the similarity of rainfall distribution; indicating the applicability of the use of synthetic rainfall series to estimate the R factor. ANNs had a better performance than Inverse Distance Weighted and Kriging to spatially interpolate rainfall erosivity values in the State of Espírito Santo.Academia Brasileira de Ciências2013-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652013000401523Anais da Academia Brasileira de Ciências v.85 n.4 2013reponame:Anais da Academia Brasileira de Ciências (Online)instname:Academia Brasileira de Ciências (ABC)instacron:ABC10.1590/0001-3765201398012info:eu-repo/semantics/openAccessCECíLIO,ROBERTO A.MOREIRA,MICHEL C.PEZZOPANE,JOSé EDUARDO M.PRUSKI,FERNANDO F.FUKUNAGA,DANILO C.eng2015-11-03T00:00:00Zoai:scielo:S0001-37652013000401523Revistahttp://www.scielo.br/aabchttps://old.scielo.br/oai/scielo-oai.php||aabc@abc.org.br1678-26900001-3765opendoar:2015-11-03T00:00Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)false
dc.title.none.fl_str_mv Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networks
title Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networks
spellingShingle Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networks
CECíLIO,ROBERTO A.
interpolation
rainfall generator
soil conservation
universal soil loss equation
title_short Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networks
title_full Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networks
title_fullStr Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networks
title_full_unstemmed Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networks
title_sort Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networks
author CECíLIO,ROBERTO A.
author_facet CECíLIO,ROBERTO A.
MOREIRA,MICHEL C.
PEZZOPANE,JOSé EDUARDO M.
PRUSKI,FERNANDO F.
FUKUNAGA,DANILO C.
author_role author
author2 MOREIRA,MICHEL C.
PEZZOPANE,JOSé EDUARDO M.
PRUSKI,FERNANDO F.
FUKUNAGA,DANILO C.
author2_role author
author
author
author
dc.contributor.author.fl_str_mv CECíLIO,ROBERTO A.
MOREIRA,MICHEL C.
PEZZOPANE,JOSé EDUARDO M.
PRUSKI,FERNANDO F.
FUKUNAGA,DANILO C.
dc.subject.por.fl_str_mv interpolation
rainfall generator
soil conservation
universal soil loss equation
topic interpolation
rainfall generator
soil conservation
universal soil loss equation
description The rainfall parameter that expresses the capacity to promote soil erosion is called rainfall erosivity (R), and is commonly represented by the indexes EI30 and KE>25. The calculations of these indexes requires pluviographical records, that are difficult to obtain in Brazil. This paper describes the use of synthetic rainfall series to compute EI30 and KE>25 in Espírito Santo State (Brazil). Artificial neural networks (ANNs) were also developed to spatially interpolate R values in Espírito Santo. EI30 and KE>25 indexes values were close to those calculated on a homogeneous area according to the similarity of rainfall distribution; indicating the applicability of the use of synthetic rainfall series to estimate the R factor. ANNs had a better performance than Inverse Distance Weighted and Kriging to spatially interpolate rainfall erosivity values in the State of Espírito Santo.
publishDate 2013
dc.date.none.fl_str_mv 2013-01-01
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=S0001-37652013000401523
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652013000401523
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0001-3765201398012
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 Academia Brasileira de Ciências
publisher.none.fl_str_mv Academia Brasileira de Ciências
dc.source.none.fl_str_mv Anais da Academia Brasileira de Ciências v.85 n.4 2013
reponame:Anais da Academia Brasileira de Ciências (Online)
instname:Academia Brasileira de Ciências (ABC)
instacron:ABC
instname_str Academia Brasileira de Ciências (ABC)
instacron_str ABC
institution ABC
reponame_str Anais da Academia Brasileira de Ciências (Online)
collection Anais da Academia Brasileira de Ciências (Online)
repository.name.fl_str_mv Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)
repository.mail.fl_str_mv ||aabc@abc.org.br
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