Probability Estimates of Short-Term Rockburst Risk with Ensemble Classifiers

Liang, Weizhang; Sari, Yuksel Asli; Zhao, Guoyan; McKinnon, Stephen D.; Wu, Hao

doi:10.1007/s00603-021-02369-3

Cited by 49 publications

(19 citation statements)

References 42 publications

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“…Five ensemble classifiers based on logistic regression, naive Bayes, Gaussian process (GP), multilayer perceptron (MLP), SVM and DT were used to estimate the occurrence probability of each risk level for short-term rockburst prediction. The results showed that the comprehensive performance of ensemble classifiers was better than each basic classifier individually [160].…”

Section: Machine Learningmentioning

confidence: 97%

Development of Rockburst Research: A Comprehensive Review

Wang

et al. 2022

Applied Sciences

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Rockburst is a difficult problem in underground resource development and infrastructure construction. With scientific and technological progress, more research technologies and methods to prevent rockburst have been proposed and applied. Research content has become more comprehensive, and positive progress and achievements have been made. However, due to the complexity of rockburst control factors and the sudden occurrence of rockburst, the occurrence mechanism and accurate prediction of occurrence intensity and time are still difficult worldwide problems. In this paper, the research development and achievements of rockburst are reviewed. Firstly, various definitions and types of rockburst are briefly summarized. Then, research progress and achievements in four aspects are comprehensively reviewed, including methods and techniques of rockburst research (theoretical research, numerical simulation, physical model tests and in situ monitoring), mechanism of rockburst, classification and prediction of rockburst (empirical criteria, in situ monitoring, mathematical model approaches and rockburst chart) and prevention and control of rockburst. Of particular significance is that the classification and prediction with prevention and control are summarized in detail. Finally, limitations, deficiencies and some promising directions for future research are listed.

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Section: Machine Learningmentioning

confidence: 97%

Development of Rockburst Research: A Comprehensive Review

Wang

et al. 2022

Applied Sciences

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“…They found that the RF was the optimal model. Liang et al [27] utilized weighting voting to combine six intelligent techniques to forecast short-term rockburst. The capacity of the comprehensive combined model was better than that of the base classifiers.…”

Section: Algorithm Superiority Drawbackmentioning

confidence: 99%

“…The ensemble model that adopted KNN and RNN performed best in all ensemble models. Although these ensemble models show a high level of prediction accuracy, some practical problems [27,28] prevent them from being widely used.…”

Section: Algorithm Superiority Drawbackmentioning

confidence: 99%

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Novel Ensemble Tree Solution for Rockburst Prediction Using Deep Forest

Liu

Armaghani

et al. 2022

Mathematics

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The occurrence of rockburst can cause significant disasters in underground rock engineering. It is crucial to predict and prevent rockburst in deep tunnels and mines. In this paper, the deficiencies of ensemble learning algorithms in rockburst prediction were investigated. Aiming at these shortages, a novel machine learning model, deep forest, was proposed to predict rockburst risk. The deep forest combines the characteristics of deep learning and ensemble models, which can solve complex problems. To develop the deep forest model for rockburst prediction, 329 real rockburst cases were collected to build a comprehensive database for intelligent analysis. Bayesian optimization was proposed to tune the hyperparameters of the deep forest. As a result, the deep forest model achieved 100% training accuracy and 92.4% testing accuracy, and it has more outstanding capability to forecast rockburst disasters compared to other widely used models (i.e., random forest, boosting tree models, neural network, support vector machine, etc.). The results of sensitivity analysis revealed the impact of variables on rockburst levels and the applicability of deep forest with a few input parameters. Eventually, real cases of rockburst in two gold mines, China, were used for validation purposes while the needed data sets were prepared by field observations and laboratory tests. The promoting results of the developed model during the validation phase confirm that it can be used with a high level of accuracy by practicing engineers for predicting rockburst occurrences.

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“…Xin et al (2021) [19] proposed an explainable time-frequency convolutional neural network (CNN) to provide an excellent classification performance and explainability. Liang et al (2021) [20] combined multiple base learners and classifiers to estimate the probability of short-term rockburst risks and achieved good performance. Saad and Chen (2020) [21] extracted waveforms from continuous microseismic data using an automatic unsupervised method, which outperformed the simple k-means and short-term and long-term average ratio methods.…”

Section: Introductionmentioning

confidence: 99%

Multi-Classification of Complex Microseismic Waveforms Using Convolutional Neural Network: A Case Study in Tunnel Engineering

Zhang

Zeng

et al. 2021

Sensors

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Due to the complexity of the various waveforms of microseismic data, there are high requirements on the automatic multi-classification of such data; an accurate classification is conducive for further signal processing and stability analysis of surrounding rock masses. In this study, a microseismic multi-classification (MMC) model is proposed based on the short time Fourier transform (STFT) technology and convolutional neural network (CNN). The real and imaginary parts of the coefficients of microseismic data are inputted to the proposed model to generate three classes of targets. Compared with existing methods, the MMC has an optimal performance in multi-classification of microseismic data in terms of Precision, Recall, and F1-score, even when the waveform of a microseismic signal is similar to that of some special noise. Moreover, semisynthetic data constructed by clean microseismic data and noise are used to prove the low sensitivity of the MMC to noise. Microseismic data recorded under different geological conditions are also tested to prove the generality of the model, and a microseismic signal with Mw ≥ 0.2 can be detected with a high accuracy. The proposed method has great potential to be extended to the study of exploration seismology and earthquakes.

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Probability Estimates of Short-Term Rockburst Risk with Ensemble Classifiers

Cited by 49 publications

References 42 publications

Development of Rockburst Research: A Comprehensive Review

Development of Rockburst Research: A Comprehensive Review

Novel Ensemble Tree Solution for Rockburst Prediction Using Deep Forest

Multi-Classification of Complex Microseismic Waveforms Using Convolutional Neural Network: A Case Study in Tunnel Engineering

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