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使用机器学习方法对一些中国煤炭的煤炭破裂责任进行分类

Chao Wang¹, Yv Liu¹, Yuefeng Li²

¹Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming, 650093, China.

Scientific reports

|June 18, 2024

概括

这项研究开发了一种优异的莱文伯格-马奎特反向传播神经网络 (LM-BPNN) 模型,用于分类煤炭爆破责任 (CBL). 该LM-BPNN模型实现了96.85%的准确性,显著改善了采矿中的煤炭爆破预防策略.

科学领域:

地质技术工程地质技术工程
采矿工程采矿工程采矿工程
计算科学计算科学

背景情况:

煤炭爆破在采矿业务中构成重大风险,需要准确分类煤炭爆破责任 (CBL).
现有的CBL评估方法需要强大可靠的预测模型来有效管理风险.

研究的目的:

为CBL分类建立一个包含关键参数的索引系统.
开发和评估用于预测CBL的机器学习模型.
确定增强煤炭爆破预防的最有效模式.

主要方法:

使用动态断裂持续时间 (DT),弹性应变能量指数 (WET),破裂能量指数 (KE) 和单轴压力强度 (RC) 创建了一个指数系统.
使用反向传播神经网络 (BPNN) 和支持矢量机器 (SVM) 开发了12个模型,使用各种优化算法.
用准确度,F1得分,卡帕系数和灵敏度分析来评估模型性能.

主要成果:

莱文伯格-马奎特反向传播神经网络 (LM-BPNN) 模型表现出卓越的性能.
LM-BPNN模型实现了96.85%的准确性,F1得分为0.9113,卡帕系数为0.9417.
在武东煤矿和伊武煤炭工业的独立验证证实LM-BPNN模型的100%准确性.

结论:

LM-BPNN模型对于CBL分类非常有效.
这种先进的模型提供了一个可行的工具,用于改进采矿业的煤炭爆破预防策略.
准确的CBL分类对于降低风险和确保运营安全至关重要.

关键词:

破裂责任分类破裂责任分类工程应用工程应用程序机器学习机器学习模型优化模型优化绩效评价绩效评价绩效评价绩效评价绩效评价

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