开发一个预测分类模型,用文字挖掘技术来确定致命矿山事故的类别
1Department of Mining Engineering, IIT (BHU), India.
International journal of occupational safety and ergonomics : JOSE
|November 1, 2025
概括
自动化文本挖掘对采矿事故进行分类,提高了安全性. 这种机器学习方法准确地对致命事件进行分类,减少了安全管理中的人工努力和人类偏见.
科学领域:
- 采矿安全工程 采矿安全工程
- 计算语言学 计算语言学
- 数据科学数据科学数据科学
背景情况:
- 采矿行业面临着高事故率,导致严重的人类痛苦和经济损失.
- 采矿事故报告的手动分类是劳动密集型和耗时的,阻碍了有效的预防策略.
研究的目的:
- 开发和评估一种新的文本挖掘方法,用于对致命矿山事故报告进行自动分类.
- 为了比较六个监督机器学习模型在分类事故类型方面的性能.
主要方法:
- 利用自然语言处理 (NLP) 将文本事故数据转换为矢量表示.
- 员工分层10倍交叉验证,以进行强大的模型培训和测试.
- 应用了六种监督机器学习模型:物流回归,SVM,随机森林,天真贝叶斯,决策树和MLP,将1308起致命事故记录分为八类.
主要成果:
- 所有模型都在分类事故类型方面表现出非常高的准确性.
- 多层感知器 (MLP) 模型获得了最高的加权平均F1得分 (0.84),其次是后勤回归 (0.83) 和SVM (0.81).
- 拟议的自动化系统为采矿安全管理提供了可靠的工具,减少了错误分类和人类偏见.
结论:
- 使用文本挖掘和机器学习的自动化系统可以有效地分类致命的采矿事故报告.
- 与手动方法相比,这种方法提高了事件分析的效率和可靠性.
- 该研究为改善采矿行业安全管理提供了宝贵的工具.
相关概念视频
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The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
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