Failure risk of Brazilian tailings dams : a data mining approach.
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Outros Autores: | |
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 |