Effects of different normalization, aggregation, and classification methods on the construction of flood vulnerability indexes
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/219557 |
Resumo: | Index-based approaches are widely employed for measuring flood vulnerability. Nevertheless, the uncertainties in the index construction are rarely considered. Here, we conducted a sensitivity analysis of a flood vulnerability index in the Maquiné Basin, Southern Brazil, considering distinct normalization, aggregation, classification methods, and their effects on the model outputs. The robustness of the results was investigated by considering Spearman’s correlations, the shift in the vulnerability rank, and spatial analysis of different normalization techniques (min-max, z-scores, distance to target, and raking) and aggregation methods (linear and geometric). The final outputs were classified into vulnerability classes using natural breaks, equal interval, quantiles, and standard deviation methods. The performance of each classification method was evaluated by spatial analysis and the Akaike’s information criterion (AIC). The results presented low sensitivity regarding the normalization step. Conversely, the geometric aggregation method produced substantial differences on the spatial vulnerability and tended to underestimate the vulnerability where indicators with low values compensated for high values. Additionally, the classification of the vulnerability into different classes led to overly sensitive outputs. Thus, given the AIC performance, the natural breaks method was most suitable. The obtained results can support decision-makers in reducing uncertainty and increasing the quality of flood vulnerability assessments. |
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Moreira, Luana LavagnoliBrito, Mariana Madruga deKobiyama, Masato2021-04-08T04:15:29Z20212073-4441http://hdl.handle.net/10183/219557001122735Index-based approaches are widely employed for measuring flood vulnerability. Nevertheless, the uncertainties in the index construction are rarely considered. Here, we conducted a sensitivity analysis of a flood vulnerability index in the Maquiné Basin, Southern Brazil, considering distinct normalization, aggregation, classification methods, and their effects on the model outputs. The robustness of the results was investigated by considering Spearman’s correlations, the shift in the vulnerability rank, and spatial analysis of different normalization techniques (min-max, z-scores, distance to target, and raking) and aggregation methods (linear and geometric). The final outputs were classified into vulnerability classes using natural breaks, equal interval, quantiles, and standard deviation methods. The performance of each classification method was evaluated by spatial analysis and the Akaike’s information criterion (AIC). The results presented low sensitivity regarding the normalization step. Conversely, the geometric aggregation method produced substantial differences on the spatial vulnerability and tended to underestimate the vulnerability where indicators with low values compensated for high values. Additionally, the classification of the vulnerability into different classes led to overly sensitive outputs. Thus, given the AIC performance, the natural breaks method was most suitable. The obtained results can support decision-makers in reducing uncertainty and increasing the quality of flood vulnerability assessments.application/pdfengWater. Basel. Vol. 13, n. 1 (Jan. 2021), [Article], 98, 16 p.Inundação : PrevençãoAnálise de riscoVulnerabilidade a desastresAggregationClassificationComposite indicatorsFlood vulnerabilityNormalizationEffects of different normalization, aggregation, and classification methods on the construction of flood vulnerability indexesEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001122735.pdf.txt001122735.pdf.txtExtracted Texttext/plain55420http://www.lume.ufrgs.br/bitstream/10183/219557/2/001122735.pdf.txt2b371c25143db51bf2d9bc7a1451667fMD52ORIGINAL001122735.pdfTexto completo (inglês)application/pdf4800434http://www.lume.ufrgs.br/bitstream/10183/219557/1/001122735.pdf16688fee81cf1dba9db8ec9b9ae16e7bMD5110183/2195572021-05-07 04:32:51.137048oai:www.lume.ufrgs.br:10183/219557Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2021-05-07T07:32:51Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Effects of different normalization, aggregation, and classification methods on the construction of flood vulnerability indexes |
title |
Effects of different normalization, aggregation, and classification methods on the construction of flood vulnerability indexes |
spellingShingle |
Effects of different normalization, aggregation, and classification methods on the construction of flood vulnerability indexes Moreira, Luana Lavagnoli Inundação : Prevenção Análise de risco Vulnerabilidade a desastres Aggregation Classification Composite indicators Flood vulnerability Normalization |
title_short |
Effects of different normalization, aggregation, and classification methods on the construction of flood vulnerability indexes |
title_full |
Effects of different normalization, aggregation, and classification methods on the construction of flood vulnerability indexes |
title_fullStr |
Effects of different normalization, aggregation, and classification methods on the construction of flood vulnerability indexes |
title_full_unstemmed |
Effects of different normalization, aggregation, and classification methods on the construction of flood vulnerability indexes |
title_sort |
Effects of different normalization, aggregation, and classification methods on the construction of flood vulnerability indexes |
author |
Moreira, Luana Lavagnoli |
author_facet |
Moreira, Luana Lavagnoli Brito, Mariana Madruga de Kobiyama, Masato |
author_role |
author |
author2 |
Brito, Mariana Madruga de Kobiyama, Masato |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Moreira, Luana Lavagnoli Brito, Mariana Madruga de Kobiyama, Masato |
dc.subject.por.fl_str_mv |
Inundação : Prevenção Análise de risco Vulnerabilidade a desastres |
topic |
Inundação : Prevenção Análise de risco Vulnerabilidade a desastres Aggregation Classification Composite indicators Flood vulnerability Normalization |
dc.subject.eng.fl_str_mv |
Aggregation Classification Composite indicators Flood vulnerability Normalization |
description |
Index-based approaches are widely employed for measuring flood vulnerability. Nevertheless, the uncertainties in the index construction are rarely considered. Here, we conducted a sensitivity analysis of a flood vulnerability index in the Maquiné Basin, Southern Brazil, considering distinct normalization, aggregation, classification methods, and their effects on the model outputs. The robustness of the results was investigated by considering Spearman’s correlations, the shift in the vulnerability rank, and spatial analysis of different normalization techniques (min-max, z-scores, distance to target, and raking) and aggregation methods (linear and geometric). The final outputs were classified into vulnerability classes using natural breaks, equal interval, quantiles, and standard deviation methods. The performance of each classification method was evaluated by spatial analysis and the Akaike’s information criterion (AIC). The results presented low sensitivity regarding the normalization step. Conversely, the geometric aggregation method produced substantial differences on the spatial vulnerability and tended to underestimate the vulnerability where indicators with low values compensated for high values. Additionally, the classification of the vulnerability into different classes led to overly sensitive outputs. Thus, given the AIC performance, the natural breaks method was most suitable. The obtained results can support decision-makers in reducing uncertainty and increasing the quality of flood vulnerability assessments. |
publishDate |
2021 |
dc.date.accessioned.fl_str_mv |
2021-04-08T04:15:29Z |
dc.date.issued.fl_str_mv |
2021 |
dc.type.driver.fl_str_mv |
Estrangeiro 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://hdl.handle.net/10183/219557 |
dc.identifier.issn.pt_BR.fl_str_mv |
2073-4441 |
dc.identifier.nrb.pt_BR.fl_str_mv |
001122735 |
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2073-4441 001122735 |
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http://hdl.handle.net/10183/219557 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
Water. Basel. Vol. 13, n. 1 (Jan. 2021), [Article], 98, 16 p. |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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