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从煤矿底层涌入的水的风险评估基于增强样本与类分布的增强样本
Shiwei Liu1,2, Jiaxin Zhao3, Hao Yu3
1College of Water Conservancy and Hydropower, Hebei University of Engineering, Handan, 056038, Hebei, China. liu1989shiwei@163.com.
Scientific reports
|January 10, 2025
概括
这项研究引入了一种新的虚拟样本增强方法,以改善煤矿地板中的水冲入风险预测. 改进后的模型大大减少了预测错误,支持更安全的采矿操作.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 地质工程是地质工程.
- 数据科学数据科学数据科学
背景情况:
- 从煤矿地板涌入的水对采矿安全构成重大风险.
- 有限和随机的现场数据阻碍了准确的预测模型开发.
- 现有的模型在小样本大小和通用性方面扎.
研究的目的:
- 开发一种可靠的方法来增强虚拟样本数据库,用于水冲入风险评估.
- 为了提高水冲入预测模型的准确性和通用性.
- 为了支持安全和高效的煤炭开采在奥尔多维纪石灰岩封闭的水上.
主要方法:
- 提出了一种使用类分布大趋势扩散技术 (CDMTD) 的虚拟样本增强方法.
- 引入了对虚拟样本生成影响因素的类分布的约束.
- 开发了一个使用合算法的预测模型:PCA-CDMTD-SaDE-ELM.
- 应用该模型来评估特定矿山工作面的水冲入风险.
主要成果:
- 该CDMTD方法有效地增强了测量数据库,并减轻了小样本大小的问题.
- 与其他优化模型相比,PCA-CDMTD-SaDE-ELM模型表现出优异的预测性能.
- 实现了42.95-51.27%的显著错误减少,结果偏向于安全.
结论:
- 拟议的基于CDMTD的虚拟样本增强对于改善水冲入风险预测是有效的.
- 结合的PCA-CDMTD-SaDE-ELM模型为评估水冲入风险提供了一个可靠的工具.
- 这些发现有助于安全和有效地利用封闭含水层上方的煤炭资源.
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