NEURO-FUZZY MODELING: A PROMISING ALTERNATIVE FOR RISK ANALYSIS IN URBAN AFFORESTATION MANAGEMENT
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 ad...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Sociedade de Investigações Florestais
2018-08-01
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| Series: | Revista Árvore |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-67622018000100205&lng=en&tlng=en |
| _version_ | 1828393052559900672 |
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| author | Adriano Bressane João Augusto Bagatini Carlos Humberto Biagolini José Arnaldo Frutuoso Roveda Sandra Regina Monteiro Masalskiene Roveda Felipe Hashimoto Fengler Regina Márcia Longo |
| author_facet | Adriano Bressane João Augusto Bagatini Carlos Humberto Biagolini José Arnaldo Frutuoso Roveda Sandra Regina Monteiro Masalskiene Roveda Felipe Hashimoto Fengler Regina Márcia Longo |
| author_sort | Adriano Bressane |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-10T07:32:12Z |
| format | Article |
| id | doaj.art-cf294efa2cc2455bb056b034922cacda |
| institution | Directory Open Access Journal |
| issn | 1806-9088 |
| language | English |
| last_indexed | 2024-12-10T07:32:12Z |
| publishDate | 2018-08-01 |
| publisher | Sociedade de Investigações Florestais |
| record_format | Article |
| series | Revista Árvore |
| spelling | doaj.art-cf294efa2cc2455bb056b034922cacda2022-12-22T01:57:32ZengSociedade de Investigações FlorestaisRevista Árvore1806-90882018-08-0142110.1590/1806-90882018000100006S0100-67622018000100205NEURO-FUZZY MODELING: A PROMISING ALTERNATIVE FOR RISK ANALYSIS IN URBAN AFFORESTATION MANAGEMENTAdriano BressaneJoão Augusto BagatiniCarlos Humberto BiagoliniJosé Arnaldo Frutuoso RovedaSandra Regina Monteiro Masalskiene RovedaFelipe Hashimoto FenglerRegina Márcia LongoABSTRACT 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.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-67622018000100205&lng=en&tlng=enRisk indicatorsIntegrated analysisUncertainties |
| spellingShingle | Adriano Bressane João Augusto Bagatini Carlos Humberto Biagolini José Arnaldo Frutuoso Roveda Sandra Regina Monteiro Masalskiene Roveda Felipe Hashimoto Fengler Regina Márcia Longo NEURO-FUZZY MODELING: A PROMISING ALTERNATIVE FOR RISK ANALYSIS IN URBAN AFFORESTATION MANAGEMENT Revista Árvore Risk indicators Integrated analysis Uncertainties |
| title | 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_short | 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 |
| topic | Risk indicators Integrated analysis Uncertainties |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-67622018000100205&lng=en&tlng=en |
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