Failure risk of Brazilian tailings dams : a data mining approach.
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Date
2021
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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 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.
Description
Keywords
Clustering problem, Based random key genetic algorithm
Citation
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.