基于瑞士-斯克拉尔规则的天然气安全情况分析
1Department of Computer Science and Technology, Xi'an University of Science and Technology, Yanta Road, Xi'an 710054, China.
本研究引入了一种用于煤矿的新型天然气安全情况分析模型 (GSSAM-SSR). 该模型通过使用多重的瑞士-斯克拉尔规则 (SSR) 组合提高了甲气体爆炸预测的准确性,提高了安全意识.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 煤炭开采产生有害气体,主要是甲,造成爆炸风险.
- 气体安全局势意识对于矿山的预警系统至关重要.
- 分类安全风险水平对于及时干预至关重要.
研究的目的:
- 开发一种气体安全情况分析模型 (GSSAM-SSR) 以提高煤矿安全.
- 通过先进的机器学习技术,提高气体风险级别分类的准确性.
- 提出一种新的组合规则,以提高预测性能.
主要方法:
- 数据预处理,包括K-means集群和主要组件分析,以减少维度.
- 使用预订方法构建和选择基本分类模型.
- 开发用于分类器集成的多重权重的Schweizer-Sklar三角规范阵列组合规则 (SSR).
- 将GSSAM-SSR模型应用于动态和准动态矿山气体数据的应用.
主要成果:
- 拟议的SSR组合规则实现了最高的分类性能.
- 该GSSAM-SSR模型的准确率为94.43%.
- 这种准确性比表现最好的个人分类器 (梯度增强决策树) 提高了2.51%.
结论:
- 采用SSR组合规则的GSSAM-SSR模型显著提高了煤矿天然气安全局势意识.
- 与现有的方法相比,该模型提供了一种更准确,更可靠的方法来分类与天然气有关的风险.
- 这一进步有助于改善地下采矿操作中的安全协议和减少爆炸风险.
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