Grade estimation using a hybrid method of back-propagation artificial neural network and particle swarm optimization with integrated samples coordinate and local variability

Saeed Soltani-Mohammadi*, Fatemeh Sadat Hoseinian, Maliheh Abbaszadeh, M. Khodadadzadeh

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
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Grade estimation is a critical issue in mineral resource evaluation, being extensively investigated by data mining techniques. In this paper, a hybrid method composed of back-propagation artificial neural network (BPANN) and particle swarm optimization (PSO) algorithms is proposed to solve the grade estimation problem. The PSO algorithm is implemented to optimize the BPANN parameters by reducing the effects of a local minimum problem, which is one of the critical drawbacks of BPANN. The proposed BPANN-PSO algorithm is validated for Al2O3 grade estimation in one of Iran's largest Bauxite deposits. The performance of BPANN-PSO algorithm for grade estimation is compared with BPANN and ordinary kriging. The experimental results indicate that the BPANN-PSO model is more appropriate for estimating Al2O3 grade with a reasonable error.

Original languageEnglish
Article number104981
Number of pages8
JournalComputers and Geosciences
Early online date9 Nov 2021
Publication statusPublished - Feb 2022


  • Grade estimation
  • Input space configuration
  • Machine learning methods
  • Parameter optimization

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