Failure risk of brazilian tailings dams: a data mining approach
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Anais da Academia Brasileira de Ciências (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000701702 |
Resumo: | Abstract 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 efficacy of the proposed method producing feasible solutions. Thus, it is expected that the good results can be applied in the identification of tailings dams with risk potentials, assisting in the identification of these dams. |
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Anais da Academia Brasileira de Ciências (Online) |
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Failure risk of brazilian tailings dams: a data mining approachTailing damsclustering problembiased random key genetic algorithmminingAbstract 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 efficacy of the proposed method producing feasible solutions. Thus, it is expected that the good results can be applied in the identification of tailings dams with risk potentials, assisting in the identification of these dams.Academia Brasileira de Ciências2021-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000701702Anais da Academia Brasileira de Ciências v.93 n.4 2021reponame:Anais da Academia Brasileira de Ciências (Online)instname:Academia Brasileira de Ciências (ABC)instacron:ABC10.1590/0001-3765202120201242info:eu-repo/semantics/openAccessSANTOS,TATIANA B.OLIVEIRA,RUDINEI M.eng2021-09-21T00:00:00Zoai:scielo:S0001-37652021000701702Revistahttp://www.scielo.br/aabchttps://old.scielo.br/oai/scielo-oai.php||aabc@abc.org.br1678-26900001-3765opendoar:2021-09-21T00:00Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)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 B. Tailing dams clustering problem biased random key genetic algorithm mining |
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 B. |
author_facet |
SANTOS,TATIANA B. OLIVEIRA,RUDINEI M. |
author_role |
author |
author2 |
OLIVEIRA,RUDINEI M. |
author2_role |
author |
dc.contributor.author.fl_str_mv |
SANTOS,TATIANA B. OLIVEIRA,RUDINEI M. |
dc.subject.por.fl_str_mv |
Tailing dams clustering problem biased random key genetic algorithm mining |
topic |
Tailing dams clustering problem biased random key genetic algorithm mining |
description |
Abstract 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 efficacy of the proposed method producing feasible solutions. Thus, it is expected that the good results can be applied in the identification of tailings dams with risk potentials, assisting in the identification of these dams. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 |
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=S0001-37652021000701702 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000701702 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0001-3765202120201242 |
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 |
Academia Brasileira de Ciências |
publisher.none.fl_str_mv |
Academia Brasileira de Ciências |
dc.source.none.fl_str_mv |
Anais da Academia Brasileira de Ciências v.93 n.4 2021 reponame:Anais da Academia Brasileira de Ciências (Online) instname:Academia Brasileira de Ciências (ABC) instacron:ABC |
instname_str |
Academia Brasileira de Ciências (ABC) |
instacron_str |
ABC |
institution |
ABC |
reponame_str |
Anais da Academia Brasileira de Ciências (Online) |
collection |
Anais da Academia Brasileira de Ciências (Online) |
repository.name.fl_str_mv |
Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC) |
repository.mail.fl_str_mv |
||aabc@abc.org.br |
_version_ |
1754302870949199872 |