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

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中文摘要：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。

外文标题：Enhancing rock fragmentation prediction in mining operations:A hybrid GWO-RF model with SHAP interpretability

外文摘要：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.

外文关键词：

blastingrock fragmentationrandom forestgrey wolf optimizationhybrid tree-based technique

作者：

张宇霖、邱引桂、ARMAGHANI Danial Jahed、MONJEZI Masoud、周健

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作者单位：

School of Resources and Safety Engineering, Central South University, Changsha 410083, China

School of Civil and Environmental Engineering,University of Technology Sydney,New South Wales 2007,Australia

Department of Mining,Faculty of Engineering,Tarbiat Modares University,Tehran 14115-143,Iran

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

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关键词：

爆破岩石块度随机森林灰狼优化器混合模型

基金：

National Science Foundation of ChinaNational Science Foundation of ChinaState Key Laboratory of Precision Blasting and Hubei Key Laboratory of Blasting Engineering,China

项目编号：

4217716452474121PBSKL2023A12

出版年：

2024

DOI：

10.1007/s11771-024-5699-z

中南大学学报(英文版)

中南大学

中南大学学报(英文版)

CSTPCDEI

影响因子：0.47

ISSN：2095-2899

年,卷(期)：2024.31(8)