Backbreak is a rock fracture problem that exceeds the limits of the last row of holes in an explosion operation. Excessive backbreak increases operational cost and also poses a threat to mine safety. In this regard, a new hybrid intelligence approach based on random forest (RF) and particle swarm optimization (PSO) are proposed for predicting backbreak with high accuracy to reduce the unsolicited phenomenon induced by backbreak in open-pit blasting. A data set of 234 samples with 6 input parameters including special drilling (SD), spacing (S), burden (B), hole length (L), stemming (T) and powder factor (PF)) and one output parameter backbreak (BB) is set up in this study. Seven input combinations (one with six parameters, six with five parameters) are built to generate the optimal prediction model. The PSO algorithm was integrated with the RF algorithm to find the optimal hyper-parameters ( [[EQUATION]] and [[EQUATION]] ) of each model and the fitness function, which is the MAE of 10 cross-validations. The performance capacities of the optimal models are assessed using mean absolute error, root mean square error, Pearson correlation coefficient and mean absolute percentage error. Findings demonstrated that the PSO-RF model combining L-S-B-T-PF with MAE of (0.0132, and 0.0568), RMSE of (0.0811, 0.1686), R 2 of (0.9990, 0.9961), and MAPE of (0.0027, and 0.0116) in training and testing phases, respectively, has optimal prediction performance. The optimal PSO-RF models are compared with the classical Artificial Neural Network (ANN), RF, Genetic Programming (GP), and Support Vector Machine (SVM) models that show that the PSO-RF model has superiority in predicting backbreak. The Gini index of each input variable has also been calculated in the RF model, which were 31.2(L), 23.1(S), 27.4(B), 36.6(T), 23.4(PF), and 16.9(SD), respectively.
