NEURO-FUZZY MODELING: A PROMISING ALTERNATIVE FOR RISK ANALYSIS IN URBAN AFFORESTATION MANAGEMENT
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista Árvore (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-67622018000100205 |
Resumo: | ABSTRACT Urban afforestation has important functions, but problems related to its management are equally relevant, analysis of which is needed in order to prevent accidents. However, due to the subjectivity in the assessment, there may be uncertainty as to the seriousness of the risk. In order to address this, the present work evaluates a neuro-fuzzy-based methodology for the integrated analysis of risk indicators. From the knowledge of experts and a database with 107 cases, systems were constructed for the multi-criteria analysis of 18 parameters integrated using 3 indexes and 5 indicators. As a result, the model presented accuracies of 95.5% in generalization tests, and almost perfect agreement (kappa > 0.8) with the assessment by the expert. In conclusion, the findings show that this neuro-fuzzy modeling approach represents a promising alternative for supporting risk analysis in urban afforestation. |
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NEURO-FUZZY MODELING: A PROMISING ALTERNATIVE FOR RISK ANALYSIS IN URBAN AFFORESTATION MANAGEMENTRisk indicatorsIntegrated analysisUncertaintiesABSTRACT Urban afforestation has important functions, but problems related to its management are equally relevant, analysis of which is needed in order to prevent accidents. However, due to the subjectivity in the assessment, there may be uncertainty as to the seriousness of the risk. In order to address this, the present work evaluates a neuro-fuzzy-based methodology for the integrated analysis of risk indicators. From the knowledge of experts and a database with 107 cases, systems were constructed for the multi-criteria analysis of 18 parameters integrated using 3 indexes and 5 indicators. As a result, the model presented accuracies of 95.5% in generalization tests, and almost perfect agreement (kappa > 0.8) with the assessment by the expert. In conclusion, the findings show that this neuro-fuzzy modeling approach represents a promising alternative for supporting risk analysis in urban afforestation.Sociedade de Investigações Florestais2018-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-67622018000100205Revista Árvore v.42 n.1 2018reponame:Revista Árvore (Online)instname:Universidade Federal de Viçosa (UFV)instacron:SIF10.1590/1806-90882018000100006info:eu-repo/semantics/openAccessBressane,AdrianoBagatini,João AugustoBiagolini,Carlos HumbertoRoveda,José Arnaldo FrutuosoRoveda,Sandra Regina Monteiro MasalskieneFengler,Felipe HashimotoLongo,Regina Márciaeng2018-08-08T00:00:00Zoai:scielo:S0100-67622018000100205Revistahttp://www.scielo.br/revistas/rarv/iaboutj.htmPUBhttps://old.scielo.br/oai/scielo-oai.php||r.arvore@ufv.br1806-90880100-6762opendoar:2018-08-08T00:00Revista Árvore (Online) - Universidade Federal de Viçosa (UFV)false |
dc.title.none.fl_str_mv |
NEURO-FUZZY MODELING: A PROMISING ALTERNATIVE FOR RISK ANALYSIS IN URBAN AFFORESTATION MANAGEMENT |
title |
NEURO-FUZZY MODELING: A PROMISING ALTERNATIVE FOR RISK ANALYSIS IN URBAN AFFORESTATION MANAGEMENT |
spellingShingle |
NEURO-FUZZY MODELING: A PROMISING ALTERNATIVE FOR RISK ANALYSIS IN URBAN AFFORESTATION MANAGEMENT Bressane,Adriano Risk indicators Integrated analysis Uncertainties |
title_short |
NEURO-FUZZY MODELING: A PROMISING ALTERNATIVE FOR RISK ANALYSIS IN URBAN AFFORESTATION MANAGEMENT |
title_full |
NEURO-FUZZY MODELING: A PROMISING ALTERNATIVE FOR RISK ANALYSIS IN URBAN AFFORESTATION MANAGEMENT |
title_fullStr |
NEURO-FUZZY MODELING: A PROMISING ALTERNATIVE FOR RISK ANALYSIS IN URBAN AFFORESTATION MANAGEMENT |
title_full_unstemmed |
NEURO-FUZZY MODELING: A PROMISING ALTERNATIVE FOR RISK ANALYSIS IN URBAN AFFORESTATION MANAGEMENT |
title_sort |
NEURO-FUZZY MODELING: A PROMISING ALTERNATIVE FOR RISK ANALYSIS IN URBAN AFFORESTATION MANAGEMENT |
author |
Bressane,Adriano |
author_facet |
Bressane,Adriano Bagatini,João Augusto Biagolini,Carlos Humberto Roveda,José Arnaldo Frutuoso Roveda,Sandra Regina Monteiro Masalskiene Fengler,Felipe Hashimoto Longo,Regina Márcia |
author_role |
author |
author2 |
Bagatini,João Augusto Biagolini,Carlos Humberto Roveda,José Arnaldo Frutuoso Roveda,Sandra Regina Monteiro Masalskiene Fengler,Felipe Hashimoto Longo,Regina Márcia |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Bressane,Adriano Bagatini,João Augusto Biagolini,Carlos Humberto Roveda,José Arnaldo Frutuoso Roveda,Sandra Regina Monteiro Masalskiene Fengler,Felipe Hashimoto Longo,Regina Márcia |
dc.subject.por.fl_str_mv |
Risk indicators Integrated analysis Uncertainties |
topic |
Risk indicators Integrated analysis Uncertainties |
description |
ABSTRACT Urban afforestation has important functions, but problems related to its management are equally relevant, analysis of which is needed in order to prevent accidents. However, due to the subjectivity in the assessment, there may be uncertainty as to the seriousness of the risk. In order to address this, the present work evaluates a neuro-fuzzy-based methodology for the integrated analysis of risk indicators. From the knowledge of experts and a database with 107 cases, systems were constructed for the multi-criteria analysis of 18 parameters integrated using 3 indexes and 5 indicators. As a result, the model presented accuracies of 95.5% in generalization tests, and almost perfect agreement (kappa > 0.8) with the assessment by the expert. In conclusion, the findings show that this neuro-fuzzy modeling approach represents a promising alternative for supporting risk analysis in urban afforestation. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-01-01 |
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=S0100-67622018000100205 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-67622018000100205 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1806-90882018000100006 |
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 |
Sociedade de Investigações Florestais |
publisher.none.fl_str_mv |
Sociedade de Investigações Florestais |
dc.source.none.fl_str_mv |
Revista Árvore v.42 n.1 2018 reponame:Revista Árvore (Online) instname:Universidade Federal de Viçosa (UFV) instacron:SIF |
instname_str |
Universidade Federal de Viçosa (UFV) |
instacron_str |
SIF |
institution |
SIF |
reponame_str |
Revista Árvore (Online) |
collection |
Revista Árvore (Online) |
repository.name.fl_str_mv |
Revista Árvore (Online) - Universidade Federal de Viçosa (UFV) |
repository.mail.fl_str_mv |
||r.arvore@ufv.br |
_version_ |
1750318002916556800 |