Evaluating the Performance of Extreme Learning Machine Technique for Ore Grade Estimation

Due to the complex geology of vein deposits and their erratic grade distributions, there is the tendency of over-estimating or underestimating the ore grade. These estimated grade results determine the pro fi tability of mining the ore deposit or otherwise. In this study, fi ve Extreme Learning Machine (ELM) variants based on hard limit, sigmoid, triangular basis, sine and radial basis activation functions were applied to predict ore grade. The motive is that the activation function has been identi fi ed to play a key role in achieving optimum ELM performance. Therefore, assessing the extent of in fl uence the activation functions will have on the fi nal outputs from the ELM has some scienti fi c value worth investigating. This study therefore applied ELM as ore grade estimator which is yet to be explored in the literature. The obtained results from the fi ve ELM variants were analysed and compared with the state-of-the-art benchmark methods of Back-propagation Neural Network (BPNN) and Ordinary Kriging (OK). The statistical test results revealed that the ELM with sigmoid activation function (ELM-Sigmoid) was the best among all the other investigated methods (ELM-Hard limit, ELM-Triangular basis, ELM-Sine, ELM-Radial Basis, BPNN and OK). This is because the ELM-sigmoid produced the lowest MAE (0.0175), MSE (0.0005) and RMSE (0.0229) with highest R 2 (91.93%) and R (95.88%) respectively. It was concluded that ELM-Sigmoid can be used by fi eld practitioners as a reliable alternative ore grade estimation technique.