基于机器学习的煤矿屋顶事故风险水平预测的研究
Zhao-Yang Guan1, Jin-Ling Xie2, Shen-Kuang Wu3
1Department of Mathematical Sciences, University of Liverpool, Liverpool, L69 3BX, UK.
Scientific reports
|July 5, 2025
概括
这项研究引入了一种新的方法来预测煤矿屋顶事故风险,提高工人的安全和生产力. 先进的随机森林模型实现了94%的准确性,大大提高了事故预防策略.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 职业安全与健康问题 职业安全与健康问题
- 数据科学和机器学习
背景情况:
- 煤矿屋顶事故造成了严重的安全和生产力损失,约占每年事故的20%.
- 有效的风险预测和管理对于工人安全和持续的煤矿运营至关重要.
- 现有的方法可能缺乏动态风险评估和预防所需的精度.
研究的目的:
- 开发和验证一种用于预测煤矿屋顶事故风险水平的新实用方法.
- 提供早期预警系统,完善管理策略和动态调整以防止事故.
- 通过技术手段提高煤矿的整体安全性和生产效率.
主要方法:
- 收集并过了379起煤矿屋顶事故案例,建立了305个分析实例的数据集.
- 利用主要组件分析 (PCA) 来减少复杂的高维事故数据的维度.
- 采用K-近邻 (KNN),支持向量机 (SVM) 和决策树 (DT) 进行初始模型评估,然后使用随机森林合并算法进行改进的预测.
主要成果:
- 随机森林模型实现了0.94的预测准确度,这与基线算法相比显著改进.
- 召回率提高到0.87,F1得分提高到0.89,在识别和分类事故风险方面表现优越.
- 拟议的方法显示了对其他类型的煤矿事故的应用潜力,有助于主动安全管理.
结论:
- 开发的风险预测方法有效提高了煤矿事故预测的准确性和可靠性.
- 随机森林整体方法为采矿环境中复杂的安全数据分析提供了强大的解决方案.
- 这种方法支持数据驱动的决策,以改善安全协议和减少煤矿事故发生率.
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