Failure risk of Brazilian tailings dams : a data mining approach.

Detalhes bibliográficos
Autor(a) principal: Santos, Tatiana Barreto dos
Data de Publicação: 2021
Outros Autores: Oliveira, Rudinei Martins de
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFOP
Texto Completo: http://www.repositorio.ufop.br/jspui/handle/123456789/15488
https://doi.org/10.1590/0001-3765202120201242
Resumo: This paper proposes the use of a hybrid method that combines Biased Random Key Genetic Algorithm (BRKGA) with a local search heuristic to separate Brazilian tailing dam data into groups. The goal was identifying dams similar to Fundão and B1 failed dams. The groups were created by solving the clustering problem by BRKGA. The clustering problem consists in separating a set of objects into groups such that members of each group are similar to each other. The data was composed by 427 dams, with the actual 425 dams of Brazilian Register of Tailing Dams and the two Brazilian failed dams from the last years. Computational experiments considering real data available are presented to demonstrate the effi cacy of the proposed method producing feasible solutions. Thus, it is expected that the good results can be applied in the identifi cation of tailings dams with risk potentials, assisting in the identifi cation of these dams.
id UFOP_408da58e31266cdb4523703d47c48f33
oai_identifier_str oai:repositorio.ufop.br:123456789/15488
network_acronym_str UFOP
network_name_str Repositório Institucional da UFOP
repository_id_str 3233
spelling Failure risk of Brazilian tailings dams : a data mining approach.Clustering problemBased random key genetic algorithmThis paper proposes the use of a hybrid method that combines Biased Random Key Genetic Algorithm (BRKGA) with a local search heuristic to separate Brazilian tailing dam data into groups. The goal was identifying dams similar to Fundão and B1 failed dams. The groups were created by solving the clustering problem by BRKGA. The clustering problem consists in separating a set of objects into groups such that members of each group are similar to each other. The data was composed by 427 dams, with the actual 425 dams of Brazilian Register of Tailing Dams and the two Brazilian failed dams from the last years. Computational experiments considering real data available are presented to demonstrate the effi cacy of the proposed method producing feasible solutions. Thus, it is expected that the good results can be applied in the identifi cation of tailings dams with risk potentials, assisting in the identifi cation of these dams.2022-09-26T19:58:19Z2022-09-26T19:58:19Z2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfSANTOS, T. B. dos; OLIVEIRA, R. M. de. Failure risk of Brazilian tailings dams: a data mining approach. Anais da Academia Brasileira de Ciências, v. 93, 2021. Disponível em: <https://www.scielo.br/j/aabc/a/qKVwsqhqmRGrY4ZypnVS6YL/abstract/?lang=en>. Acesso em: 29 abr. 2022.1678-2690http://www.repositorio.ufop.br/jspui/handle/123456789/15488https://doi.org/10.1590/0001-3765202120201242This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY). Fonte: o PDF do artigo.info:eu-repo/semantics/openAccessSantos, Tatiana Barreto dosOliveira, Rudinei Martins deengreponame:Repositório Institucional da UFOPinstname:Universidade Federal de Ouro Preto (UFOP)instacron:UFOP2022-09-26T19:58:27Zoai:repositorio.ufop.br:123456789/15488Repositório InstitucionalPUBhttp://www.repositorio.ufop.br/oai/requestrepositorio@ufop.edu.bropendoar:32332022-09-26T19:58:27Repositório Institucional da UFOP - Universidade Federal de Ouro Preto (UFOP)false
dc.title.none.fl_str_mv Failure risk of Brazilian tailings dams : a data mining approach.
title Failure risk of Brazilian tailings dams : a data mining approach.
spellingShingle Failure risk of Brazilian tailings dams : a data mining approach.
Santos, Tatiana Barreto dos
Clustering problem
Based random key genetic algorithm
title_short Failure risk of Brazilian tailings dams : a data mining approach.
title_full Failure risk of Brazilian tailings dams : a data mining approach.
title_fullStr Failure risk of Brazilian tailings dams : a data mining approach.
title_full_unstemmed Failure risk of Brazilian tailings dams : a data mining approach.
title_sort Failure risk of Brazilian tailings dams : a data mining approach.
author Santos, Tatiana Barreto dos
author_facet Santos, Tatiana Barreto dos
Oliveira, Rudinei Martins de
author_role author
author2 Oliveira, Rudinei Martins de
author2_role author
dc.contributor.author.fl_str_mv Santos, Tatiana Barreto dos
Oliveira, Rudinei Martins de
dc.subject.por.fl_str_mv Clustering problem
Based random key genetic algorithm
topic Clustering problem
Based random key genetic algorithm
description This paper proposes the use of a hybrid method that combines Biased Random Key Genetic Algorithm (BRKGA) with a local search heuristic to separate Brazilian tailing dam data into groups. The goal was identifying dams similar to Fundão and B1 failed dams. The groups were created by solving the clustering problem by BRKGA. The clustering problem consists in separating a set of objects into groups such that members of each group are similar to each other. The data was composed by 427 dams, with the actual 425 dams of Brazilian Register of Tailing Dams and the two Brazilian failed dams from the last years. Computational experiments considering real data available are presented to demonstrate the effi cacy of the proposed method producing feasible solutions. Thus, it is expected that the good results can be applied in the identifi cation of tailings dams with risk potentials, assisting in the identifi cation of these dams.
publishDate 2021
dc.date.none.fl_str_mv 2021
2022-09-26T19:58:19Z
2022-09-26T19:58:19Z
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 SANTOS, T. B. dos; OLIVEIRA, R. M. de. Failure risk of Brazilian tailings dams: a data mining approach. Anais da Academia Brasileira de Ciências, v. 93, 2021. Disponível em: <https://www.scielo.br/j/aabc/a/qKVwsqhqmRGrY4ZypnVS6YL/abstract/?lang=en>. Acesso em: 29 abr. 2022.
1678-2690
http://www.repositorio.ufop.br/jspui/handle/123456789/15488
https://doi.org/10.1590/0001-3765202120201242
identifier_str_mv SANTOS, T. B. dos; OLIVEIRA, R. M. de. Failure risk of Brazilian tailings dams: a data mining approach. Anais da Academia Brasileira de Ciências, v. 93, 2021. Disponível em: <https://www.scielo.br/j/aabc/a/qKVwsqhqmRGrY4ZypnVS6YL/abstract/?lang=en>. Acesso em: 29 abr. 2022.
1678-2690
url http://www.repositorio.ufop.br/jspui/handle/123456789/15488
https://doi.org/10.1590/0001-3765202120201242
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFOP
instname:Universidade Federal de Ouro Preto (UFOP)
instacron:UFOP
instname_str Universidade Federal de Ouro Preto (UFOP)
instacron_str UFOP
institution UFOP
reponame_str Repositório Institucional da UFOP
collection Repositório Institucional da UFOP
repository.name.fl_str_mv Repositório Institucional da UFOP - Universidade Federal de Ouro Preto (UFOP)
repository.mail.fl_str_mv repositorio@ufop.edu.br
_version_ 1813002798652981248