Application of Interpretable Machine Learning for Production Feasibility Prediction of Gold Mine Project

Abstract

In the context of globalization in the mining industry, assessing the production feasibility of mining projects by smart technology is crucial for the improvement of mining development efficiency. However, evaluating the feasibility of such projects faces significant challenges due to incomplete data and complex variables. In recent years, the development of big data technology has offered new possibilities for rapidly evaluating mining projects. This study conducts an intelligent evaluation of gold mines based on global mineral resources data to estimate whether a gold mine project can be put into production. A technical workflow is constructed, including data filling, evaluation model construction, and production feasibility evaluation. Based on the workflow, the missing data is filled in by the Miceforest imputation algorithm first. The evaluation model is established based on the Random Forest model to quantitatively predict the feasibility of the mining project being put into production, and important features of the model are extracted using Shapley Additive explanation(SHAP). This workflow may enhance the efficiency and accuracy of quantitative production feasibility evaluation for mining projects, with an accuracy rate increased from 93.80% to 95.99%. Results suggest that the features of estimated mine life and gold ore grade have the most significant impact on production feasibility.

Keywords

• gold mine project
• production feasibility
• miceforest imputation
• random forest
• SHAP value