Comparing the predictive performance of neural networks with ordinary kriging in a bauxite deposit

This paper investigates the performance of neural network and ordinary kriging techniques in estimating five variables: accumulated Al2O3, accumulated SiO2, percentage Al2O3, percentage SiO2, and thickness of a bauxite deposit in India. The assay values obtained from exploratory boreholes were compiled according to the geological composition of the deposit. Genetic algorithms were used to divide the dataset into model development and evaluation subsets, ensuring that the modelling subset and evaluation subset were similar and that performance evaluation was valid. The results indicate that neural networks and ordinary kriging performed equally well in this deposit, except for the variable accumulated Al2O3. However, the coefficients of determination (R-2) of predictions were not very good.