Evaluation of Predictive Models for the Optimization of the Cost of Unit Operations in Artisanal Underground Mining

The objective of this study is to evaluate and optimize the costs of unit operations in artisanal underground mining through the application of predictive models based on machine learning. Four models were trained and validated: Multiple Linear Regression (MLR), Random Forest (RF), Decision Tree (DT), and Artificial Neural Networks (ANN), using a dataset that includes operational, geological, and economic variables collected over a three-month period. Among the evaluated models, Decision Trees demonstrated the best performance, achieving a coefficient of determination R^2of 0.90, which enabled the identification of optimal cost parameters for critical activities such as drilling and blasting (41.32 US$/tn), shoveling (17.03 US$/tn), hauling (4.13 US$/tn), loading (16.08 US$/tn), ventilation (8.81 US$/tn), and ground support (1.51 US$/tn). Exceeding these costs results in cost overruns in unit operations, negatively impacting the profitability of the company. The results of this study provide an innovative approach to cost optimization in artisanal underground mining, enhancing profitability and contributing to more informed decision-making. The implications of these findings suggest immediate applications and open new opportunities for future research in other mining contexts.