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基于因子优化和随机森林方法的煤层屋顶含水层水富的评估
Guichao Gai1, Mei Qiu2, Weiqiang Zhang3
1College of Earth Science & Engineering, Shandong University of Science and Technology, Qingdao, 266590, Shandong, China.
评估煤层屋顶的水富对于预防危险至关重要. 这项研究使用斯皮尔曼相关性和GeoDetector优化了因素,发现随机森林模型最准确地预测了水的丰富性.
科学领域:
- 地质地质地质地质地质地
- 采矿工程 采矿工程 采矿工程
- 水文地质学 水文地质学
背景情况:
- 防止煤层中的水危险需要精确的水富评估.
- 石学和结构因素显著影响煤层屋顶的水富.
研究的目的:
- 调查岩石学和结构因素对煤层屋顶水富的意义.
- 为了比较不同水资源丰富度评估模型的预测准确度.
- 优化因子选择以提高模型性能.
主要方法:
- 斯皮尔曼相关性分析以确定重要的影响因素.
- 地质探测器用于分析因子相互作用并选择最佳组合.
- 重法 (EWM),变量系数法 (CVM) 和随机森林法 (RFM) 用于模型开发.
- AICc用于模型优越性确定.
- 通过试验,工面水流量试验和3D电气方法进行验证.
主要成果:
- 发现有6个岩石学和结构因素显著影响水的丰富性.
- 通过GeoDetector分析确定了三个关键组合因素.
- 与EWM和CVM相比,随机森林方法 (RFM) 显示出更高的预测准确性.
- 因子优化提高了所有评估模型的准确性.
结论:
- 优化因素选择和先进的建模技术,如RFM,改善了煤层屋顶的水富评估.
- 准确的水资源丰富度评估对于减轻地下采矿中与水有关的风险至关重要.
- 该研究为预测和管理煤炭开采业务中的水危险提供了一个强大的框架.
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