School of Resources and Environmental Engineering, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, China.
Wuhan Safety and Environmental Protection Research Institute of Sinosteel Group, Wuhan, 430081, Hubei, China.
Sci Rep. 2022 Sep 12;12(1):15352. doi: 10.1038/s41598-022-19669-5.
Rockburst forecasting plays a crucial role in prevention and control of rockburst disaster. To improve the accuracy of rockburst prediction at the data structure and algorithm levels, the Yeo-Johnson transform, K-means SMOTE oversampling, and optimal rockburst feature dimension determination are used to optimize the data structure. At the algorithm optimization level, ensemble stacking rockburst prediction is performed based on the data structure optimization. First, to solve the problem of many outliers and data imbalance in the distribution of rockburst data, the Yeo-Johnson transform and k-means SMOTE algorithm are respectively used to solve the problems. Then, based on six original rockburst features, 21 new features are generated using the PolynomialFeatures function in Sklearn. Principal component analysis (PCA) dimensionality reduction is applied to eliminate the correlations between the 27 features. Thirteen types of machine learning algorithms are used to predict datasets that retain different numbers of features after dimensionality reduction to determine the optimal rockburst feature dimension. Finally, the 14-feature rockburst dataset is used as the input for integrated stacking. The results show that the ensemble stacking model based on Yeo-Johnson, K-means SMOTE, and optimal rockburst feature dimension determination can improve the accuracy of rockburst prediction by 0.1602-0.3636. Compared with the 13 single machine learning models without data preprocessing, this data structure optimization and algorithm optimization method effectively improves the accuracy of rockburst prediction.
岩爆预测在岩爆灾害的防治中起着至关重要的作用。为了提高数据结构和算法层面的岩爆预测精度，采用了约翰逊变换、K-means SMOTE 过采样和最优岩爆特征维度确定来优化数据结构。在算法优化层面，基于数据结构优化进行集成堆叠岩爆预测。首先，为了解决岩爆数据分布中存在的大量异常值和数据不平衡问题，分别采用了约翰逊变换和 K-means SMOTE 算法进行处理。然后，基于六个原始岩爆特征，使用 Sklearn 中的 PolynomialFeatures 函数生成了 21 个新特征。应用主成分分析（PCA）降维来消除 27 个特征之间的相关性。使用 13 种机器学习算法来预测降维后保留不同数量特征的数据集，以确定最优的岩爆特征维度。最后，将 14 特征岩爆数据集作为集成堆叠的输入。结果表明，基于约翰逊变换、K-means SMOTE 和最优岩爆特征维度确定的集成堆叠模型可以将岩爆预测的准确率提高 0.1602-0.3636。与没有数据预处理的 13 个单机器学习模型相比，这种数据结构优化和算法优化方法有效地提高了岩爆预测的准确性。
