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Metadados

1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Sitemtc-m21c.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34R/3T3CHRB
Repositóriosid.inpe.br/mtc-m21c/2019/04.01.11.30   (acesso restrito)
Última Atualização2019:04.01.11.30.46 (UTC) simone
Repositório de Metadadossid.inpe.br/mtc-m21c/2019/04.01.11.30.46
Última Atualização dos Metadados2020:01.06.11.42.12 (UTC) administrator
DOI10.1002/hyp.13388
ISSN0885-6087
Chave de CitaçãoCassalhoBeMeMoOlAg:2019:StReFl
TítuloArtificial intelligence for identifying hydrologically homogeneous regions: A state-of-the-art regional flood frequency analysis
Ano2019
Mêsmar.
Data de Acesso09 maio 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho783 KiB
2. Contextualização
Autor1 Cassalho, Felicio
2 Beskow, Samuel
3 Mello, Carlos Rogério de
4 Moura, Maíra Martim de
5 Oliveira, Leroi Floriano
6 Aguiar, Marilton Sanchotene de
ORCID1 0000-0001-9496-2910
2 0000-0003-3900-0895
3 0000-0002-6033-5342
Grupo1 SER-SRE-SESPG-INPE-MCTIC-GOV-BR
Afiliação1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Universidade Federal de Pelotas (UFPEL)
3 Universidade Federal de Lavras (UFLA)
4 Universidade Federal de Pelotas (UFPEL)
5 Universidade Federal de Pelotas (UFPEL)
6 Universidade Federal de Pelotas (UFPEL)
Endereço de e-Mail do Autor1 felicio.cassalho@inpe.br
RevistaHydrological Processes
Volume33
Número7
Páginas1101-1116
Nota SecundáriaA1_ENGENHARIAS_III A1_ENGENHARIAS_I A1_CIÊNCIAS_AMBIENTAIS A2_INTERDISCIPLINAR A2_GEOCIÊNCIAS A2_CIÊNCIAS_AGRÁRIAS_I A2_BIODIVERSIDADE B2_CIÊNCIA_DA_COMPUTAÇÃO
Histórico (UTC)2019-04-01 11:30:46 :: simone -> administrator ::
2019-04-01 11:30:47 :: administrator -> simone :: 2019
2019-04-01 11:31:34 :: simone -> administrator :: 2019
2020-01-06 11:42:12 :: administrator -> simone :: 2019
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
Tipo de Versãopublisher
Palavras-Chavecluster analysis
evolutionary computation
fuzzy logic
heterogeneity measure
index-flood
L-moments
ResumoDue to the severity related to extreme flood events, recent efforts have focused on the development of reliable methods for design flood estimation. Historical streamflow series correspond to the most reliable information source for such estimation; however, they have temporal and spatial limitations that may be minimized by means of regional flood frequency analysis (RFFA). Several studies have emphasized that the identification of hydrologically homogeneous regions is the most important and challenging step in an RFFA. This study aims to identify state-of-the-art clustering techniques (e.g., K-means, partition around medoids, fuzzy C-means, K-harmonic means, and genetic K-means) with potential to form hydrologically homogeneous regions for flood regionalization in Southern Brazil. The applicability of some probability density function, such as generalized extreme value, generalized logistic, generalized normal, and Pearson type 3, was evaluated based on the regions formed. Among all the 15 possible combinations of the aforementioned clustering techniques and the Euclidian, Mahalanobis, and Manhattan distance measures, the five best were selected. Several watersheds' physiographic and climatological attributes were chosen to derive multiple regression equations for all the combinations. The accuracy of the equations was quantified with respect to adjusted coefficient of determination, root mean square error, and Nash-Sutcliffe coefficient, whereas, a cross-validation procedure was applied to check their reliability. It was concluded that reliable results were obtained when using robust clustering techniques based on fuzzy logic (e.g., K-harmonic means), which have not been commonly used in RFFA. Furthermore, the probability density functions were capable of representing the regional annual maximum streamflows. Drainage area, main river length, and mean altitude of the watershed were the most recurrent attributes for modelling of mean annual maximum streamflow. Finally, an integration of all the five best combinations stands out as a robust, reliable, and simple tool for estimation of design floods.
ÁreaSRE
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4. Condições de acesso e uso
Idiomaen
Arquivo AlvoCassalho_et_al-2019-Hydrological_Processes.pdf
Grupo de Usuáriossimone
Grupo de Leitoresadministrator
simone
Visibilidadeshown
Política de Arquivamentodenypublisher denyfinaldraft
Permissão de Leituradeny from all and allow from 150.163
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/3F3NU5S
Lista de Itens Citandosid.inpe.br/bibdigital/2013/10.18.22.34 3
DivulgaçãoWEBSCI; PORTALCAPES; COMPENDEX; SCOPUS.
Acervo Hospedeirourlib.net/www/2017/11.22.19.04
6. Notas
Campos Vaziosalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes parameterlist parentrepositories previousedition previouslowerunit progress project resumeid rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url
7. Controle da descrição
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