Shahrood University of Technology Journal of Mining and Environment 2251-8592 15 4 2024 10 01 Predicting Open Pit Mine Production using Machine Learning Techniques: A Case Study in Peru 1345 1355 3157 10.22044/jme.2024.14416.2703 EN Marco Antonio Cotrina Teatino Department of Mining Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru Jairo Jhonatan Marquina Araujo Department of Mining Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru Eduardo Manuel Noriega Vidal Department of Mining Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru Jose Nestor Mamani Quispe Department of Mining Engineering, University of Chile, Santiago, Chile Johnny Henrry Ccatamayo Barrios Department of Mining Engineering, National University of San Cristóbal de Huamanga, Ayacucho, Peru Joe Alexis Gonzalez Vasquez Department of Industrial Engineering, National University of Trujillo, Trujillo, Peru Solio Marino Arango Retamozo Department of Industrial Engineering, National University of Trujillo, Trujillo, Peru 0000-0003-3594-0329 Journal Article 2024 04 13 The primary objective of this research was to apply machine learning techniques to predict the production of an open pit mine in Peru. Four advanced techniques were employed: Random Forest (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Bayesian Regression (RB). The methodology included the collection of 90 datasets over a three-month period, encompassing variables such as operational delays, operating hours, equipment utilization, the number of dump trucks used, and daily production. The data were allocated 70% for training and 30% for testing. The models were evaluated using metrics such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Variance Accounted For (VAF), and the Coefficient of Determination (R²). The results indicated that the Bayesian Regression model was the most effective in predicting production in the open pit mine. The RMSE, MAPE, VAF, and R² for the models were 3686.60, 3581.82, 4576.61, and 3352.87; 12.65, 11.09, 15.31, and 11.90; 36.82, 40.72, 1.85, and 47.32; 0.37, 0.41, 0.41, and 0.47 for RF, XGBoost, KNN, and RB, respectively. This research highlights the efficacy of machine learning techniques in predicting mine production and recommends adjusting each model's parameters to further enhance outcomes, significantly contributing to strategic and operational management in the mining industry. https://jme.shahroodut.ac.ir/article_3157_23d8329aa120ef78d705535d3ff9692f.pdf