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

• Published in: Computers & geosciences 2022-02, Vol.159, p.104981, Article 104981
• Main Authors: Soltani-Mohammadi, Saeed, Hoseinian, Fatemeh Sadat, Abbaszadeh, Maliheh, Khodadadzadeh, Mahdi
• Format: Article
• Language: English
• Subjects: Grade estimation; Input space configuration; Machine learning methods; Parameter optimization
• ISSN: 0098-3004; 1873-7803
• DOI: 10.1016/j.cageo.2021.104981

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. •3D block model is estimated by a hybrid model of BPANN and PSO algorithms.•Samples coordinate and local variability are used in the BPANN architecture.•The initial weight and threshold value of the back-propagation artificial neural network are optimized by PSO.•The proposed model showed better performance compared to the ordinary kriging and BPANN models in the presented case study.