基于K-means SMOTE和IDBO-RF岩爆烈度等级预测模型
Prediction model of rockburst intensity levels based on K-means SMOTE and IDBO-RF
温廷新 1王泽锋2
作者信息
- 1. 辽宁工程技术大学工商管理学院,辽宁葫芦岛 125100;辽宁工程技术大学鄂尔多斯研究院,内蒙古鄂尔多斯 017000
- 2. 辽宁工程技术大学工商管理学院,辽宁葫芦岛 125100
- 折叠
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 op-timizer,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 pre-dictive 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.Subse-quently,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 pre-diction model(IDBO-RF).The model's effectiveness is verified through comparison with other models.The research find-ings 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,respec-tively.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.
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Key words
data balancing/improved dung beetle optimizer(IDBO)/random forest/rockburst intensity level/pre-diction model引用本文复制引用
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)
出版年
2024
NETL
NSTL
万方数据