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: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1590/1806-90882018000100006 http://hdl.handle.net/11449/164523 |
Resumo: | 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 analysisUncertaintiesUrban 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.Univ Estadual Paulista, Dept Engn Ambiental, Sao Jose Dos Campos, SP, BrazilPrefeitura Municipal Nova Prata, Nova Prata, RS, BrazilUniv Estadual Paulista, Campus Sorocaba, Sorocaba, SP, BrazilUniv Estadual Paulista, Dept Engn Ambiental, Sorocaba, SP, BrazilUniv Catolica Campinas, Fac Engn Ambiental, Campinas, SP, BrazilUniv Estadual Paulista, Dept Engn Ambiental, Sao Jose Dos Campos, SP, BrazilUniv Estadual Paulista, Campus Sorocaba, Sorocaba, SP, BrazilUniv Estadual Paulista, Dept Engn Ambiental, Sorocaba, SP, BrazilUniv Federal VicosaUniversidade Estadual Paulista (Unesp)Prefeitura Municipal Nova PrataUniv Catolica CampinasBressane, Adriano [UNESP]Bagatini, Joao AugustoBiagolini, Carlos Humberto [UNESP]Frutuoso Roveda, Jose Arnaldo [UNESP]Monteiro Masalskiene Roveda, Sandra Regina [UNESP]Fengler, Felipe Hashimoto [UNESP]Longo, Regina Marcia2018-11-26T17:54:54Z2018-11-26T17:54:54Z2018-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article10application/pdfhttp://dx.doi.org/10.1590/1806-90882018000100006Revista Arvore. Vicosa: Univ Federal Vicosa, v. 42, n. 1, 10 p., 2018.0100-6762http://hdl.handle.net/11449/16452310.1590/1806-90882018000100006S0100-67622018000100205WOS:000441757200002S0100-67622018000100205.pdf89596375594042060000-0002-4899-3983Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengRevista Arvore0,458info:eu-repo/semantics/openAccess2023-10-12T06:08:12Zoai:repositorio.unesp.br:11449/164523Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-10-12T06:08:12Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)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 [UNESP] 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 [UNESP] |
author_facet |
Bressane, Adriano [UNESP] Bagatini, Joao Augusto Biagolini, Carlos Humberto [UNESP] Frutuoso Roveda, Jose Arnaldo [UNESP] Monteiro Masalskiene Roveda, Sandra Regina [UNESP] Fengler, Felipe Hashimoto [UNESP] Longo, Regina Marcia |
author_role |
author |
author2 |
Bagatini, Joao Augusto Biagolini, Carlos Humberto [UNESP] Frutuoso Roveda, Jose Arnaldo [UNESP] Monteiro Masalskiene Roveda, Sandra Regina [UNESP] Fengler, Felipe Hashimoto [UNESP] Longo, Regina Marcia |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Prefeitura Municipal Nova Prata Univ Catolica Campinas |
dc.contributor.author.fl_str_mv |
Bressane, Adriano [UNESP] Bagatini, Joao Augusto Biagolini, Carlos Humberto [UNESP] Frutuoso Roveda, Jose Arnaldo [UNESP] Monteiro Masalskiene Roveda, Sandra Regina [UNESP] Fengler, Felipe Hashimoto [UNESP] Longo, Regina Marcia |
dc.subject.por.fl_str_mv |
Risk indicators Integrated analysis Uncertainties |
topic |
Risk indicators Integrated analysis Uncertainties |
description |
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 |
2018-11-26T17:54:54Z 2018-11-26T17:54:54Z 2018-01-01 |
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://dx.doi.org/10.1590/1806-90882018000100006 Revista Arvore. Vicosa: Univ Federal Vicosa, v. 42, n. 1, 10 p., 2018. 0100-6762 http://hdl.handle.net/11449/164523 10.1590/1806-90882018000100006 S0100-67622018000100205 WOS:000441757200002 S0100-67622018000100205.pdf 8959637559404206 0000-0002-4899-3983 |
url |
http://dx.doi.org/10.1590/1806-90882018000100006 http://hdl.handle.net/11449/164523 |
identifier_str_mv |
Revista Arvore. Vicosa: Univ Federal Vicosa, v. 42, n. 1, 10 p., 2018. 0100-6762 10.1590/1806-90882018000100006 S0100-67622018000100205 WOS:000441757200002 S0100-67622018000100205.pdf 8959637559404206 0000-0002-4899-3983 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Revista Arvore 0,458 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
10 application/pdf |
dc.publisher.none.fl_str_mv |
Univ Federal Vicosa |
publisher.none.fl_str_mv |
Univ Federal Vicosa |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
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1803045956453662720 |