A hybrid ensemble model of kriging and neural network for ore grade estimation

Abstract

This paper presents a new hybrid methodology involving kriging and artificial neural network for ore grade estimation of two variables namely, Al2O3% and SiO2%, in a bauxite deposit. The dataset was divided into three statistically similar subsets: training, calibration and validation sets using a genetic algorithm. The proposed hybrid ensemble model was formed using a kriging model and several neural network models. The outputs of these component models were combined using two methods to produce a unified prediction. The results indicated that the hybrid model was not a better estimator than the kriging model for the variable Al2O3%. However, it provides slightly better performance in comparison to any of the other component models in the ensemble for the variable SiO2%.