Failure risk of brazilian tailings dams: a data mining approach

Detalhes bibliográficos
Autor(a) principal: SANTOS,TATIANA B.
Data de Publicação: 2021
Outros Autores: OLIVEIRA,RUDINEI M.
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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spelling 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
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dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652021000701702
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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
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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)
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repository.name.fl_str_mv Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)
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