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[1]温廷新,王泽锋.基于K-means SMOTE和IDBO-RF岩爆烈度等级预测模型*[J].中国安全生产科学技术,2024,20(6):140-146.[doi:10.11731/j.issn.1673-193x.2024.06.019]
　WEN Tingxin,WANG Zefeng.Prediction model of rockburst intensity levels based on K-means SMOTE and IDBO-RF[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2024,20(6):140-146.[doi:10.11731/j.issn.1673-193x.2024.06.019]
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基于K-means SMOTE和IDBO-RF岩爆烈度等级预测模型*
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
20
期数:
2024年6期
页码:
140-146
栏目:
职业安全卫生管理与技术
出版日期:
2024-06-30

文章信息/Info

Title:
Prediction model of rockburst intensity levels based on K-means SMOTE and IDBO-RF
文章编号:
1673-193X(2024)-06-0140-07
作者:
温廷新; 王泽锋
（1.辽宁工程技术大学 工商管理学院，辽宁 葫芦岛 125100；
2.辽宁工程技术大学 鄂尔多斯研究院，内蒙古 鄂尔多斯 017000）
Author(s):
WEN Tingxin;  WANG Zefeng
(1.School of Business Administration,Liaoning Technical University,Huludao Liaoning 125100,China;
2.Ordos Institute,Liaoning Technical University,Ordos Inner Mongolia 017000,China)
关键词:
数据均衡; 改进蜣螂优化（IDBO）; 随机森林; 岩爆烈度等级; 预测模型
Keywords:
data balancing;  improved dung beetle optimizer (IDBO);  random forest;  rockburst intensity level;  pre-diction model
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2024.06.019
文献标志码:
A
摘要:
为解决岩爆数据集不均衡和模型参数寻优困难等问题，提出1种基于K-means SMOTE与改进蜣螂算法优化随机森林（random forest,RF）的预测模型。首先，分析岩爆发生机理构建指标体系；其次，使用K-means SMOTE算法对岩爆数据集进行均衡化处理，采用Robust标准化消除量纲；最后，引入Tent混沌映射和非线性递减策略组合改进蜣螂优化（improved dung beetle optimizer,IDBO）算法，寻优RF超参数，建立岩爆烈度等级预测模型（IDBO-RF）并与其他模型对比验证其有效性。研究结果表明：数据均衡处理后，各模型准确率提高10.85%~16.02%；设计的IDBO-RF预测模型平均准确率约为94.37%，较RF、GWO-RF、DBO-RF模型分别提高约7.76百分点、1.69百分点、1.11百分点；IDBO-RF预测模型准确率最高约为96.43%，优于RF、GWO-RF、DBO-RF模型。研究结果可为解决岩爆预测问题提供一定参考。
Abstract:
To address the issues of imbalanced rockburst datasets and the challenges in optimizing model parameters,a predictive model based on K-means SMOTE and an improved dung beetle optimizer (IDBO) algorithm for optimizing random forest (RF) is proposed.Initially,the mechanism of rockburst occurrence is analyzed to construct an indicator system.Subsequently,the K-means SMOTE algorithm is employed to balance the rockburst dataset,and Robust Standardization is used to eliminate dimensionality.Finally,the Tent chaotic map and a nonlinear decreasing strategy are incorporated to improve the dung beetle optimizer algorithm for optimizing RF hyperparameters,resulting in the establishment of a rockburst intensity prediction model (IDBO-RF).The model’s effectiveness is verified through comparison with other models.The research findings indicate that,following data balancing,the accuracy of various models improves by 10.85% to 16.02%.The designed IDBO-RF prediction model achieves an average accuracy of approximately 94.37%,which is an improvement of about 7.76 percentage point,1.69 percentage point,and 1.11 percentage point over the RF,GWO-RF,and DBO-RF models,respectively.The IDBO-RF prediction model attains the highest accuracy of approximately 96.43%,outperforming the RF,GWO-RF,and DBO-RF models.These results can provide reference for solving the problem of rockburst prediction.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期： 2023-11-15
* 基金项目： 国家自然科学基金项目（71371091）；辽宁省社会科学规划基金项目（L14BTJ004）
作者简介： 温廷新，博士，教授，主要研究方向为矿业系统工程、数据分析与智能决策。
通信作者： 王泽锋，硕士研究生，主要研究方向为数据挖掘。
更新日期/Last Update: 2024-06-25