General regression neural network residual estimation for ore grade prediction of limestone deposit

The aim of the present paper is to provide an improved estimator for the ore grade prediction of a limestone deposit in India. A generalised modelling framework with the help of general regression neural network and the ordinary kriging was formulated to capture the spatial variability of the deposit. In this platform, spatial variability of the deposit is assumed to be characterised by three major components: spatial trend component, regionalised component, and purely random component. The general regression neural network (GRNN) model was used to capture the spatial trend component, and the ordinary kriging technique was implemented to capture the regionalised component. The GRNN model was developed using the spatial coordinates (Northing, Easting and Elevation) as the input parameters and the grade attributes (CaO, Al2O3, Fe2O3 and SiO2) as the output parameters. The performance of the GRNN residual kriging model was tested using a testing data set, and the outputs of this model were compared with the outputs of the GRNN model, and the ordinary kriging. The comparative results show that the GRNN residual kriging model provided significant improvement over the ordinary kriging, however, it only shows a marginal improvement over the GRNN model.