Rockburst Intensity Grade Prediction Based on Data Preprocessing Techniques and Multi-model Ensemble Learning Algorithms

Jia, Zhi-Chao; Wang, Yi; Wang, Jun-Hui; Pei, Qiu-Yan; Zhang, Yan-Qi

doi:10.1007/s00603-024-03811-y

Cited by 3 publications

(1 citation statement)

References 70 publications

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“…Hence, researchers endeavor to integrate artificial intelligence techniques into rockburst prediction endeavors [8]. Traditional machine learning algorithms such as support vector machine [9,10], decision tree [11], and random forest [12,13] have also been employed to address the nonlinear challenges inherent in rockburst prediction. Furthermore, deep learning methods have garnered attention due to their enhanced nonlinear processing capabilities.…”

Section: Introductionmentioning

confidence: 99%

Distriformer: Research on a Distributed Training Rockburst Prediction Method

Zhang,

Fang,

Guo

2024

Processes

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The precise forecasting of rockburst is fundamental for safeguarding human lives and property, upholding national energy security, and protecting social welfare. Traditional methods for predicting rockburst suffer from poor accuracy and extended model training durations. This paper proposes a distributed training rockburst prediction method called Distriformer, which uses deep learning technology combined with distributed training methods to predict rockburst. To assess the efficacy of the Distriformer rockburst model proposed herein, five datasets were used to compare the proposed method with Transformer and Informer. The experimental results indicate that, compared with Transformer, the proposed method reduces the mean absolute error by 44.4% and the root mean square error by 30.7% on average. In terms of training time, the proposed method achieves an average accelaration ratio of 1.72. The Distriformer rockburst model enhances the accuracy of rockburst prediction, reduces training time, and serves as a reference for development of subsequent real-time prediction models for extensive rockburst data.

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Section: Introductionmentioning

confidence: 99%

Distriformer: Research on a Distributed Training Rockburst Prediction Method

Zhang,

Fang,

Guo

2024

Processes

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CHIM-Net: A Combined Hierarchical Information Model for Predicting Time, Space and Intensity of Mining Microseismic Events

Luo,

Zhang,

Pan

et al. 2024

Rock Mech Rock Eng

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Rock dynamic strength prediction in cold regions using optimized hybrid algorithmic models

Lv,

Shen,

Zhang

et al. 2024

Geomech. Geophys. Geo-energ. Geo-resour.

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Predicting the dynamic mechanical characteristics of rocks during freeze–thaw cycles (FTC) is crucial for comprehending the damage process of FTC and averting disasters in rock engineering in cold climates. Nevertheless, the conventional mathematical regression approach has constraints in accurately forecasting the dynamic compressive strength (DCS) of rocks under these circumstances. Hence, this study presents an optimized approach by merging the Coati Optimization Algorithm (COA) with Random Forest (RF) to offer a reliable solution for nondestructive prediction of DCS of rocks in cold locations. Initially, a database of the DCS of rocks after a series of FTC was constructed, and these data were obtained by performing the Split Hopkinson Pressure Bar Test on rocks after FTC. The main influencing factors of the test can be summarized into 10, and PCA was employed to decrease the number of dimensions in the dataset, and the microtests were used to explain the mechanism of the main influencing factors. Additionally, the Backpropagation Neural Network and RF are used to construct the prediction model of DCS of rock, and six optimization techniques were employed for optimizing the hyperparameters of the model. Ultimately, the 12 hybrid prediction models underwent a thorough and unbiased evaluation utilizing a range of evaluation indicators. The outcomes of the research concluded that the COA-RF model is most recommended for application in engineering practice, and it achieved the highest score of 10 in the combined score of the training and testing phases, with the lowest RMSE (4.570,8.769), the lowest MAE (3.155,5.653), the lowest MAPE (0.028,0.050), the highest R2 (0.983,0.94).

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Rockburst Intensity Grade Prediction Based on Data Preprocessing Techniques and Multi-model Ensemble Learning Algorithms

Cited by 3 publications

References 70 publications

Distriformer: Research on a Distributed Training Rockburst Prediction Method

Distriformer: Research on a Distributed Training Rockburst Prediction Method

CHIM-Net: A Combined Hierarchical Information Model for Predicting Time, Space and Intensity of Mining Microseismic Events

Rock dynamic strength prediction in cold regions using optimized hybrid algorithmic models

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