Prediction of rockhead using a hybrid N-XGBoost machine learning framework
Prediction of rockhead using a hybrid N-XGBoost machine learning framework
复制标题
使用混合 N-XGBoost 机器学习框架预测岩头
DOI：
10.1016/j.jrmge.2021.06.012
复制
发表时间：
2021
影响因子：
7.3
通讯作者：
K. Chiam
中科院分区：
文献类型：
--
作者：
Xing Zhu;Jian Chu;Kangda Wang;Shifan Wu;Wei Yan;K. Chiam
岩体的空间信息对于隧道或地下工程的设计和施工至关重要。虽然传统的现场勘察方法（即钻孔）可以提供当地的工程地质信息，但由于其空间变化和很大的不确定性，在有限的钻孔数据的情况下准确预测岩头位置仍然具有挑战性。随着计算机科学的发展，机器学习（ML）已被证明是一种很有前途的方法，以避免人类的主观判断，并自动建立与海量数据的复杂关系。然而，很少有研究报告采用ML模型的预测岩头的位置。本文提出了一种基于空间地理信息的岩体分布预测的鲁棒概率ML模型。自然梯度增强（NGBoost）算法结合极端梯度增强（XGBoost）的框架被用作基本学习器。XGBoost模型还与其他一些ML模型进行了比较，如梯度增强回归树（GBRT），光梯度增强机（LightGBM），多元线性回归（MLR），人工神经网络（ANN）和支持向量机（SVM）。结果表明，XGBoost算法，概率N-XGBoost模型的核心算法，优于其他传统的ML模型的决定系数（R2）为0.89和均方根误差（RMSE）为5.8米的岩头位置预测的基础上有限的钻孔数据。概率N-XGBoost模型不仅实现了更高的预测精度，而且还提供了对不确定性的预测估计。因此，建议的N-XGBoost概率模型有可能被用作一个可靠的和有效的ML算法在岩石和岩土工程中的岩头位置的预测。
The spatial information of rockhead is crucial for the design and construction of tunneling or underground excavation. Although the conventional site investigation methods (i.e. borehole drilling) could provide local engineering geological information, the accurate prediction of the rockhead position with limited borehole data is still challenging due to its spatial variation and great uncertainties involved. With the development of computer science, machine learning (ML) has been proved to be a promising way to avoid subjective judgments by human beings and to establish complex relationships with mega data automatically. However, few studies have been reported on the adoption of ML models for the prediction of the rockhead position. In this paper, we proposed a robust probabilistic ML model for predicting the rockhead distribution using the spatial geographic information. The framework of the natural gradient boosting (NGBoost) algorithm combined with the extreme gradient boosting (XGBoost) is used as the basic learner. The XGBoost model was also compared with some other ML models such as the gradient boosting regression tree (GBRT), the light gradient boosting machine (LightGBM), the multivariate linear regression (MLR), the artificial neural network (ANN), and the support vector machine (SVM). The results demonstrate that the XGBoost algorithm, the core algorithm of the probabilistic N-XGBoost model, outperformed the other conventional ML models with a coefficient of determination (R2) of 0.89 and a root mean squared error (RMSE) of 5.8 m for the prediction of rockhead position based on limited borehole data. The probabilistic N-XGBoost model not only achieved a higher prediction accuracy, but also provided a predictive estimation of the uncertainty. Thus, the proposed N-XGBoost probabilistic model has the potential to be used as a reliable and effective ML algorithm for the prediction of rockhead position in rock and geotechnical engineering.