Predicting Dilution in Underground Mines with Stacking Artificial Intelligence Models and Genetic Algorithms

Dilution in underground mining refers to the unintended incorporation of waste material into the ore, reducing ore grade, revenue, and operational safety. Unplanned dilution, specifically, occurs due to overbreak caused by blasting inefficiencies or poor rock stability. To mitigate this issue, various factors related to rock quality, stope geometry, drilling, and blasting must be carefully considered. This study introduces a statistically rigorous methodology for the prediction of dilution in underground mining operations. The proposed framework incorporates a 10-fold cross-validation procedure with 30 repetitions, along with the application of nonparametric statistical tests. A total of eight supervised machine learning algorithms were investigated, with their hyperparameters systematically optimized using two distinct genetic algorithm (GA) strategies evaluated under varying population sizes. The models include support vector machines, neural networks, and tree-based approaches. Using a dataset of 120 samples, the results indicate that the GA-ANN model outperforms other approaches, achieving MAE, R2, and RMSE values of 0.2986, 0.8457, and 0.3928 for the training dataset, and 0.1882, 0.9508, and 0.2283 for the testing dataset, respectively. Furthermore, four stacking models were constructed by aggregating the top-performing base learners, giving rise to ensemble metamodels applied, for the first time, to the task of dilution prediction in underground mining.