3746 Integrating Bayesian, Ensemble, and Hybrid Machine Learning Models for Predicting Overbreak and Underbreak in Underground Stope Mining
DOI:
https://doi.org/10.17159/Abstract
Overbreak and underbreak in underground stoping operations cause significant challenges, impacting safety and operational efficiency by leading to dilution and ore loss. This study employs Machine Learning methods to predict overbreak and underbreak at individual drillhole locations, enabling dynamic prediction of stope shapes before blasting in underground mining. Based on a comprehensive database collected from a longhole sublevel stoping mine in Western Australia, different ML-based models, including Multiple Linear Regression, Random Forest Regression, Gradient Boosting Machine, and Gaussian Process Regression, were developed systematically. The best-performing model was the initial GPR with the Matern 3/2 kernel, achieving R² value of 0.820 and a Root Mean Squared Error value of 0.452. A series of hybrid models were also developed to improve prediction accuracy by combining GPR, GBR, RFR, and Artificial Neural Network with linear meta-learners. The best hybrid configuration achieved with R² value of 0.721 and RMSE value of 0.425, but further complexity led to overfitting with no gains in generalisation. SHapley Additive exPlanations analysis across all models highlighted the Mathews Stability Number, Ratio of Burden to Spacing and Void Ratio as the most influential variables, varying in impact between the RFR, GBM, and GPR models.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Sina Taheri, Erkan Topal, Roohollah Shirani Faradonbeh, Hyongdoo Jang
This work is licensed under a Creative Commons Attribution 4.0 International License.
THE INSTITUTE, AS A BODY, IS NOT RESPONSIBLE FOR THE STATEMENTS AND OPINIONS ADVANCED IN ANY OF ITS PUBLICATIONS.
Copyright© 1978 by The Southern African Institute of Mining and Metallurgy. All rights reserved. Multiple copying of the contents of this publication or parts thereof without permission is in breach of copyright, but permission is hereby given for the copying of titles and abstracts of papers and names of authors. Permission to copy illustrations and short extracts from the text of individual contributions is usually given upon written application to the Institute, provided that the source (and where appropriate, the copyright) is acknowledged. Apart from any fair dealing for the purposes of review or criticism under The Copyright Act no. 98, 1978, Section 12, of the Republic of South Africa, a single copy of an article may be supplied by a library for the purposes of research or private study. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means without the prior permission of the publishers. Multiple copying of the contents of the publication without permission is always illegal.
U.S. Copyright Law applicable to users In the U.S.A.
The appearance of the statement of copyright at the bottom of the first page of an article appearing in this journal indicates that the copyright holder consents to the making of copies of the article for personal or internal use. This consent is given on condition that the copier pays the stated fee for each copy of a paper beyond that permitted by Section 107 or 108 of the U.S. Copyright Law. The fee is to be paid through the Copyright Clearance Center, Inc., Operations Center, P.O. Box 765, Schenectady, New York 12301, U.S.A. This consent does not extend to other kinds of copying, such as copying for general distribution, for advertising or promotional purposes, for creating new collective works, or for resale.
