TY - JOUR
T1 - Grade estimation using a hybrid method of back-propagation artificial neural network and particle swarm optimization with integrated samples coordinate and local variability
AU - Soltani-Mohammadi, Saeed
AU - Hoseinian, Fatemeh Sadat
AU - Abbaszadeh, Maliheh
AU - Khodadadzadeh, M.
N1 - Funding Information:
The authors are indebted to Dr Ute Mueller and the anonymous reviewers for their valuable and constructive comments on an earlier draft of this paper. The studies reported in this manuscript were supported by a grant from the University of Kashan (Grant No. 986017 ).
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2022/2
Y1 - 2022/2
N2 - 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.
AB - 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.
KW - 2022 OA procedure
KW - Input space configuration
KW - Machine learning methods
KW - Parameter optimization
KW - ITC-ISI-JOURNAL-ARTICLE
KW - Grade estimation
U2 - 10.1016/j.cageo.2021.104981
DO - 10.1016/j.cageo.2021.104981
M3 - Article
AN - SCOPUS:85118827414
SN - 0098-3004
VL - 159
JO - Computers and Geosciences
JF - Computers and Geosciences
M1 - 104981
ER -