根据FPS-DT模型识别矿山水爆发的水源
Kaide Liu1, Yu Xia2, Xiaolong Li3
1Shaanxi Key Laboratory of Safety and Durability of Concrete Structures, Xijing University, Xi'an, 710123, China. liukaide2006@163.com.
Scientific reports
|July 27, 2025
概括
这项研究引入了一种新的FPS-DT模型,用于准确识别煤矿水源,大大提高了安全性并防止了水危险. 该模型实现了93%的准确性,超过了传统方法.
科学领域:
- 水文地质学 水文地质学
- 数据科学数据科学数据科学
- 采矿工程 采矿工程 采矿工程
背景情况:
- 与水有关的事故在煤矿中构成重大风险.
- 准确识别水源对于矿山安全和运营连续性至关重要.
研究的目的:
- 开发和验证一个先进的模型,用于识别煤矿的水源.
- 提高地下环境中的水源识别系统的准确性和稳定性.
主要方法:
- 用水层的水化学特征来分析水源.
- 在数据分类和特征提取中使用模糊C-means (FCM) 集群和主要组件分析 (PCA).
- 应用了SMOTE算法用于类不平衡,并使用CART算法构建了一个决策树模型 (FPS-DT).
主要成果:
- FPS-DT模型实现了93%的平均分类准确度.
- 与基于PCA的决策树模型相比,在识别准确度和概括能力方面表现出显著的优势 (准确度为78%).
结论:
- FPS-DT模型为煤矿水源识别提供了卓越的性能.
- 该模型提供可解释的分类规则和强大的适应性,用于在复杂的地下环境中实时识别.
- 为煤矿安全和防止水危险提供理论支持和技术保证.
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