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Intelligent prediction of rockburst based on Copula-MC oversampling architecture
Abstract An unbalanced rockburst dataset will restrict the accuracy and reliability of rockburst prediction based on machine learning. Therefore, a new oversampling algorithm was proposed based on Copula theory and Monte Carlo simulation to balance the dataset. This paper collected 243 rockburst cases worldwide. The predictors of rockburst used in this paper include the maximum tangential stress of the surrounding rock, the uniaxial compressive strength of rock, the tensile strength of rock, and the elastic energy index. During oversampling, Copula theory determined the predictors' joint distribution function by considering the correlation among predictors. Then Monte Carlo simulation was performed to generate enough data for oversampling according to rockburst classification standard. Six common machine learning methods with tenfold cross-validation were adopted to establish nonlinear models between predictors and rockburst grades. The accuracy rate of rockburst prediction has increased by 9.3–15.5% after oversampling. The prediction performance is better than the commonly used synthetic minority oversampling technique. Finally, rockburst predictors' importance was evaluated with the initial dataset, and the elastic energy index got the maximum value of 0.41. The proposed oversampling algorithm in this paper can reasonably overcome class imbalance and improve the prediction performance for rockburst.
Intelligent prediction of rockburst based on Copula-MC oversampling architecture
Abstract An unbalanced rockburst dataset will restrict the accuracy and reliability of rockburst prediction based on machine learning. Therefore, a new oversampling algorithm was proposed based on Copula theory and Monte Carlo simulation to balance the dataset. This paper collected 243 rockburst cases worldwide. The predictors of rockburst used in this paper include the maximum tangential stress of the surrounding rock, the uniaxial compressive strength of rock, the tensile strength of rock, and the elastic energy index. During oversampling, Copula theory determined the predictors' joint distribution function by considering the correlation among predictors. Then Monte Carlo simulation was performed to generate enough data for oversampling according to rockburst classification standard. Six common machine learning methods with tenfold cross-validation were adopted to establish nonlinear models between predictors and rockburst grades. The accuracy rate of rockburst prediction has increased by 9.3–15.5% after oversampling. The prediction performance is better than the commonly used synthetic minority oversampling technique. Finally, rockburst predictors' importance was evaluated with the initial dataset, and the elastic energy index got the maximum value of 0.41. The proposed oversampling algorithm in this paper can reasonably overcome class imbalance and improve the prediction performance for rockburst.
Intelligent prediction of rockburst based on Copula-MC oversampling architecture
Xue, Yiguo (author) / Li, Guangkun (author) / Li, Zhiqiang (author) / Wang, Peng (author) / Gong, Huimin (author) / Kong, Fanmeng (author)
2022
Article (Journal)
Electronic Resource
English
BKL:
56.00$jBauwesen: Allgemeines
/
38.58
Geomechanik
/
38.58$jGeomechanik
/
56.20
Ingenieurgeologie, Bodenmechanik
/
56.00
Bauwesen: Allgemeines
/
56.20$jIngenieurgeologie$jBodenmechanik
RVK:
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