先进的机器学习技术用于预测印度开放式煤矿的倾倒坡度稳定性
Arun Kumar Sahoo1,2, Debi Prasad Tripathy3, Singam Jayanthu3
1Department of Mining Engineering, National Institute of Technology, Rourkela, Odisha, 769008, India. sahooarunkumar31@gmail.com.
Scientific reports
|November 20, 2025
概括
印度的开放式煤矿开采产生了容易发生不稳定的大型垃圾填埋场. 这项研究将机器学习与地质技术数据相结合,以准确预测倾倒坡度的稳定性,提高采矿操作的安全性.
科学领域:
- 地质技术工程 地质技术工程
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 印度的开放式煤炭开采产生了大量的过载垃圾场,这给稳定性带来了重大挑战.
- 倾倒坡度稳定性分析的传统方法是复杂的,耗时的,需要先进的计算方法.
- 确保这些垃圾场的稳定性对于采矿业的运营安全和环境保护至关重要.
研究的目的:
- 提高印度开放式煤矿倾斜坡稳定性预测的准确性和可靠性.
- 将先进的机器学习技术与传统的地球技术工程方法相结合.
- 通过统计评估,确定影响倾倒坡度稳定的最有影响力的参数.
主要方法:
- 创建了一个包含2250条条目的综合数据集,包含六个关键参数:凝聚力 (c),内部摩擦角度 (φ),单位重量 (γ),整体长椅高度 (H),自然水分含量 (m) 和整体斜率角度 (β).
- 他们使用了各种机器学习模型,包括梯度增强 (GBM),轻梯度增强机 (LGBM),极端梯度增强 (XGB),直方图梯度增强 (HGB),Nu-Support向量回归器 (NuSVR),额外树回归器 (ETR),堆叠组合和H2OAutoML.
- 模型性能经过严格评估,使用R平方,MSE,MAPE,RMSE和MAE等指标. 使用沙普利增量解释 (SHAP) 技术来确定输入特征的重要性.
主要成果:
- 与其他整体模型相比,H2OAutoML模型在预测倾倒坡度的安全因子 (FOS) 中表现出更好的表现.
- 该研究成功地通过SHAP分析确定了影响倾倒坡度稳定的关键输入参数.
- 机器学习的整合显著提高了倾倒坡度稳定性预测的准确性和适用性.
结论:
- 复杂的机器学习方法提供了一种强大而有效的方法,用于分析露天煤矿的倾倒坡度稳定性.
- 开发的模型为地质工程师提供了一种可靠的工具,以评估和减轻与垃圾填埋场不稳定性相关的风险.
- 这项研究强调了人工智能驱动解决方案的潜力,以提高采矿部门的安全性和运营效率.
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