增强采矿作业中岩石块度预测:利用混合GWO-RF模型及SHAP可解释性分析
Enhancing rock fragmentation prediction in mining operations:A hybrid GWO-RF model with SHAP interpretability
张宇霖 1邱引桂 1ARMAGHANI Danial Jahed 2MONJEZI Masoud 3周健4
作者信息
- 1. School of Resources and Safety Engineering, Central South University, Changsha 410083, China
- 2. School of Civil and Environmental Engineering,University of Technology Sydney,New South Wales 2007,Australia
- 3. Department of Mining,Faculty of Engineering,Tarbiat Modares University,Tehran 14115-143,Iran
- 4. School of Resources and Safety Engineering,Central South University,Changsha 410083,China;State Key Laboratory of Precision Blasting,Jianghan University,Wuhan 430056,China;Hubei Key Laboratory of Blasting Engineering,Jianghan University,Wuhan 430056,China
- 折叠
摘要
在采矿业中,准确预测岩石爆破碎片对于优化爆破过程至关重要.本研究旨在开发一种GWO-RF的智能预测模型来增强岩石块度评估.该模型结合了灰狼优化(GWO)算法和随机森林(RF)技术,用于预测岩石破碎质量的关键参数D80值.本研究使用了来自伊朗Sarcheshmeh铜矿的数据集,并设置了六种不同的群体规模来构建GWO-RF混合模型.在已建立的搜索空间内,本研究系统地优化了 GWO-RF模型的超参数,并使用了多个评估指标.结果表明,GWO-RF混合模型在准确性方面超过了传统模型.此外,本文利用SHAP值解释了模型的贡献因子.
Abstract
In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hybrid predictive model named GWO-RF.This model combines the grey wolf optimization(GWO)algorithm with the random forest(RF)technique to predict the D80 value,a critical parameter in evaluating rock fragmentation quality.The study is conducted using a dataset from Sarcheshmeh Copper Mine,employing six different swarm sizes for the GWO-RF hybrid model construction.The GWO-RF model's hyperparameters are systematically optimized within established bounds,and its performance is rigorously evaluated using multiple evaluation metrics.The results show that the GWO-RF hybrid model has higher predictive skills,exceeding traditional models in terms of accuracy.Furthermore,the interpretability of the GWO-RF model is enhanced through the utilization of SHapley Additive exPlanations(SHAP)values.The insights gained from this research contribute to optimizing blasting operations and rock fragmentation outcomes in the mining industry.
关键词
爆破/岩石块度/随机森林/灰狼优化器/混合模型Key words
blasting/rock fragmentation/random forest/grey wolf optimization/hybrid tree-based technique引用本文复制引用
基金项目
National Science Foundation of China(42177164)
National Science Foundation of China(52474121)
State Key Laboratory of Precision Blasting and Hubei Key Laboratory of Blasting Engineering,China(PBSKL2023A12)
出版年
2024
NETL
NSTL
万方数据