A new approach for computing a flood vulnerability index using cluster analysis
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10174/19871 https://doi.org/http://dx.doi.org/10.1016/j.pce.2016.04.003 |
Resumo: | A Flood Vulnerability Index (FloodVI) was developed using Principal Component Analysis (PCA) and a new aggregation method based on Cluster Analysis (CA). PCA simplifies a large number of variables into a few uncorrelated factors representing the social, economic, physical and environmental dimensions of vulnerability. CA groups areas that have the same characteristics in terms of vulnerability into vulnerability classes. The grouping of the areas determines their classification contrary to other aggregation methods in which the areas' classification determines their grouping. While other aggregation methods distribute the areas into classes, in an artificial manner, by imposing a certain probability for an area to belong to a certain class, as determined by the assumption that the aggregation measure used is normally distributed, CA does not constrain the distribution of the areas by the classes. FloodVI was designed at the neighbourhood level and was applied to the Portuguese municipality of Vila Nova de Gaia where several flood events have taken place in the recent past. The FloodVI sensitivity was assessed using three different aggregation methods: the sum of component scores, the first component score and the weighted sum of component scores. The results highlight the sensitivity of the FloodVI to different aggregation methods. Both sum of component scores and weighted sum of component scores have shown similar results. The first component score aggregation method classifies almost all areas as having medium vulnerability and finally the results obtained using the CA show a distinct differentiation of the vulnerability where hot spots can be clearly identified. The information provided by records of previous flood events corroborate the results obtained with CA, because the inundated areas with greater damages are those that are identified as high and very high vulnerability areas by CA. This supports the fact that CA provides a reliable FloodVI. |
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A new approach for computing a flood vulnerability index using cluster analysisAggregation methodsCluster analysis;Flood vulnerability indexPrincipal components analysisA Flood Vulnerability Index (FloodVI) was developed using Principal Component Analysis (PCA) and a new aggregation method based on Cluster Analysis (CA). PCA simplifies a large number of variables into a few uncorrelated factors representing the social, economic, physical and environmental dimensions of vulnerability. CA groups areas that have the same characteristics in terms of vulnerability into vulnerability classes. The grouping of the areas determines their classification contrary to other aggregation methods in which the areas' classification determines their grouping. While other aggregation methods distribute the areas into classes, in an artificial manner, by imposing a certain probability for an area to belong to a certain class, as determined by the assumption that the aggregation measure used is normally distributed, CA does not constrain the distribution of the areas by the classes. FloodVI was designed at the neighbourhood level and was applied to the Portuguese municipality of Vila Nova de Gaia where several flood events have taken place in the recent past. The FloodVI sensitivity was assessed using three different aggregation methods: the sum of component scores, the first component score and the weighted sum of component scores. The results highlight the sensitivity of the FloodVI to different aggregation methods. Both sum of component scores and weighted sum of component scores have shown similar results. The first component score aggregation method classifies almost all areas as having medium vulnerability and finally the results obtained using the CA show a distinct differentiation of the vulnerability where hot spots can be clearly identified. The information provided by records of previous flood events corroborate the results obtained with CA, because the inundated areas with greater damages are those that are identified as high and very high vulnerability areas by CA. This supports the fact that CA provides a reliable FloodVI.2017-01-19T15:05:24Z2017-01-192016-04-22T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/19871http://hdl.handle.net/10174/19871https://doi.org/http://dx.doi.org/10.1016/j.pce.2016.04.003engPaulo Fernandez , Sandra Mourato, Madalena Moreira, Luísa Pereira (2016) A new approach for computing a flood vulnerability index using cluster analysis. Physics and Chemistry of the Earth, Parts A/B/C. Volume 94, August 2016, Pages 47–55ndndmmvmv@uevora.ptnd472Fernandez, PauloMourato, SandraMoreira, MadalenaPereira, Luisainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-03T19:09:21Zoai:dspace.uevora.pt:10174/19871Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:11:27.905093Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A new approach for computing a flood vulnerability index using cluster analysis |
title |
A new approach for computing a flood vulnerability index using cluster analysis |
spellingShingle |
A new approach for computing a flood vulnerability index using cluster analysis Fernandez, Paulo Aggregation methods Cluster analysis; Flood vulnerability index Principal components analysis |
title_short |
A new approach for computing a flood vulnerability index using cluster analysis |
title_full |
A new approach for computing a flood vulnerability index using cluster analysis |
title_fullStr |
A new approach for computing a flood vulnerability index using cluster analysis |
title_full_unstemmed |
A new approach for computing a flood vulnerability index using cluster analysis |
title_sort |
A new approach for computing a flood vulnerability index using cluster analysis |
author |
Fernandez, Paulo |
author_facet |
Fernandez, Paulo Mourato, Sandra Moreira, Madalena Pereira, Luisa |
author_role |
author |
author2 |
Mourato, Sandra Moreira, Madalena Pereira, Luisa |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Fernandez, Paulo Mourato, Sandra Moreira, Madalena Pereira, Luisa |
dc.subject.por.fl_str_mv |
Aggregation methods Cluster analysis; Flood vulnerability index Principal components analysis |
topic |
Aggregation methods Cluster analysis; Flood vulnerability index Principal components analysis |
description |
A Flood Vulnerability Index (FloodVI) was developed using Principal Component Analysis (PCA) and a new aggregation method based on Cluster Analysis (CA). PCA simplifies a large number of variables into a few uncorrelated factors representing the social, economic, physical and environmental dimensions of vulnerability. CA groups areas that have the same characteristics in terms of vulnerability into vulnerability classes. The grouping of the areas determines their classification contrary to other aggregation methods in which the areas' classification determines their grouping. While other aggregation methods distribute the areas into classes, in an artificial manner, by imposing a certain probability for an area to belong to a certain class, as determined by the assumption that the aggregation measure used is normally distributed, CA does not constrain the distribution of the areas by the classes. FloodVI was designed at the neighbourhood level and was applied to the Portuguese municipality of Vila Nova de Gaia where several flood events have taken place in the recent past. The FloodVI sensitivity was assessed using three different aggregation methods: the sum of component scores, the first component score and the weighted sum of component scores. The results highlight the sensitivity of the FloodVI to different aggregation methods. Both sum of component scores and weighted sum of component scores have shown similar results. The first component score aggregation method classifies almost all areas as having medium vulnerability and finally the results obtained using the CA show a distinct differentiation of the vulnerability where hot spots can be clearly identified. The information provided by records of previous flood events corroborate the results obtained with CA, because the inundated areas with greater damages are those that are identified as high and very high vulnerability areas by CA. This supports the fact that CA provides a reliable FloodVI. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-04-22T00:00:00Z 2017-01-19T15:05:24Z 2017-01-19 |
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://hdl.handle.net/10174/19871 http://hdl.handle.net/10174/19871 https://doi.org/http://dx.doi.org/10.1016/j.pce.2016.04.003 |
url |
http://hdl.handle.net/10174/19871 https://doi.org/http://dx.doi.org/10.1016/j.pce.2016.04.003 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Paulo Fernandez , Sandra Mourato, Madalena Moreira, Luísa Pereira (2016) A new approach for computing a flood vulnerability index using cluster analysis. Physics and Chemistry of the Earth, Parts A/B/C. Volume 94, August 2016, Pages 47–55 nd nd mmvmv@uevora.pt nd 472 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799136596941864960 |