Abstract
Rockburst is a common dynamic geological hazard, severely restricting the development and utilization of underground space and resources. As the depth of excavation and mining increases, rockburst tends to occur frequently. Hence, it is necessary to carry out a study on rockburst prediction. Due to the nonlinear relationship between rockburst and its influencing factors, artificial intelligence was introduced. However, the collected data were typically imbalanced. Single algorithms trained by such data have low recognition for minority classes. In order to handle the problem, this paper employed stacking technique of ensemble learning to establish rockburst prediction models. In total, 246 sets of data were collected. In the preprocessing stage, three data mining techniques including principal component analysis, local outlier factor and expectation maximization algorithm were used for dimension reduction, outlier detection and outlier substitution, respectively. Then, the pre-processed data were split into a training set (75%) and a test set (25%) with stratified sampling. Based on the four classical single intelligent algorithms, namely k-nearest neighbors (KNN), support vector machine (SVM), deep neural network (DNN) and recurrent neural network (RNN), four ensemble models (KNN–RNN, SVM–RNN, DNN–RNN and KNN–SVM–DNN–RNN) were built by stacking technique of ensemble learning. The prediction performance of eight models was evaluated, and the differences between single models and ensemble models were analyzed. Additionally, a sensitivity analysis was conducted, revealing the importance of input variables on the models. Finally, the impact of class imbalance on the prediction accuracy and fitting effect of models was quantitatively discussed. The results showed that stacking technique of ensemble learning provides a new and promising way for rockburst prediction, which exhibits unique advantages especially when using imbalanced data.
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References
Adoko, A. C., Gokceoglu, C., Wu, L., & Zuo, Q. J. (2013). Knowledge-based and data-driven fuzzy modeling for rockburst prediction. International Journal of Rock Mechanics and Mining Sciences, 61, 86–95.
Afraei, S., Shahriar, K., & Madani, S. H. (2019). Developing intelligent classification models for rock burst prediction after recognizing significant predictor variables, section 1: Literature review and data preprocessing procedure. Tunnelling and Underground Space Technology, 83, 324–353.
Baltz, R., & Hucke, A. (2008). Rockburst prevention in the German coal industry. In Proceedings of the 27th international conference on ground control in mining (pp. 46–50).
Barton, N. (2002). Some new Q-value correlations to assist in site characterisation and tunnel design. International Journal of Rock Mechanics and Mining Sciences, 39(2), 185–216.
Branco, P., Torgo, L., & Ribeiro, R. P. (2017). Relevance-based evaluation metrics for multi-class imbalanced domains. In Advances in knowledge discovery and data mining (pp. 698–710).
Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.
Breiman, L. (2000). Randomizing outputs to increase prediction accuracy. Machine Learning, 40(3), 229–242.
Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. In Proceedings of the 2000 ACM SIGMOD international conference on management of data (pp. 93–104).
Cai, W., Dou, L. M., Si, G. Y., Cao, A. Y., He, J., & Liu, S. (2016). A principal component analysis/fuzzy comprehensive evaluation model for coal burst liability assessment. International Journal of Rock Mechanics and Mining Sciences, 81, 62–69.
Cai, W., Dou, L., Zhang, M., Cao, W., Shi, J., & Feng, L. (2018). A fuzzy comprehensive evaluation methodology for rock burst forecasting using microseismic monitoring. Tunnelling and Underground Space Technology, 80, 232–245.
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2011). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16(1), 321–357.
Corteza, P., & Embrechtsb, M. J. (2013). Using sensitivity analysis and visualization techniques to open black box data mining models. Information Sciences, 225, 1–17.
Daskalaki, S., Kopanas, I., & Avouris, N. (2006). Evaluation of classifiers for an uneven class distribution problem. Applied Artificial Intelligence, 20(5), 381–417.
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39(1), 1–38.
Dietterich, T. G. (2000). Ensemble methods in machine learning. In International workshop on multiple classifier systems (pp. 1–15).
Díez-Pastor, J. F., Rodríguez, J. J., García-Osorio, C., & Kuncheva, L. I. (2015). Random balance: Ensembles of variable priors classifiers for imbalanced data. Knowledge-Based Systems, 85, 96–111.
Dong, L. J., Li, X. B., & Peng, K. (2013). Prediction of rockburst classification using random forest. Transactions of Nonferrous Metals Society of China, 23(2), 472–477.
Feng, X. T., & Wang, L. (1994). Rockburst prediction based on neural networks. Transactions of Nonferrous Metals Society of China, 4(1), 7–14.
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
Ganganwar, V. (2012). An overview of classification algorithms for imbalanced datasets. International Journal of Emerging Technology and Advanced Engineering, 2(4), 42–47.
He, J., Dou, L. M., Gong, S. Y., Li, J., & Ma, Z. Q. (2017). Rock burst assessment and prediction by dynamic and static stress analysis based on micro-seismic monitoring. International Journal of Rock Mechanics and Mining Sciences, 93, 46–53.
Hoek, E., & Brown, E. T. (1980). Underground excavations in rock. London: The Institution of Mining and Metallurgy.
Hossin, M., & Sulaiman, M. (2015). A review on evaluation metrics for data classification evaluations. International Journal of Data Mining & Knowledge Management Process, 5(2), 1.
Jia, Y., Lu, Q., & Shang, Y. (2013). Rockburst prediction using particle swarm optimization algorithm and general regression neural network. Chinese Journal of Rock Mechanics and Engineering, 32(2), 343–348.
Kautz, T., Eskofier, B. M., & Pasluosta, C. F. (2017). Generic performance measure for multiclass-classifiers. Pattern Recognition, 68, 111–125.
Knorr, E. M., & Ng, R. T. (1998). Algorithms for mining distance-based outliers in large datasets. In Proceedings of the 24th VLDB conference (pp. 392–403).
Li, N., & Jimenez, R. (2017). A logistic regression classifier for long-term probabilistic prediction of rock burst hazard. Natural Hazards, 90(1), 197–215.
Li, N., Jimenez, R., & Feng, X. D. (2017a). The influence of bayesian networks structure on rock burst hazard prediction with incomplete data. Procedia Engineering, 191, 206–214.
Li, T. Z., Li, Y. X., & Yang, X. L. (2017b). Rock burst prediction based on genetic algorithms and extreme learning machine. Journal of Central South University, 24(9), 2105–2113.
Luque, A., Carrasco, A., Martín, A., & de las Heras, A. (2019). The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recognition, 91, 216–231.
Ma, T. H., Tang, C. A., Tang, S. B., Kuang, L., Yu, Q., Kong, D. Q., et al. (2018). Rockburst mechanism and prediction based on microseismic monitoring. International Journal of Rock Mechanics and Mining Sciences, 110, 177–188.
Mohamed Salleh, F. H., Arif, S. M., Zainudin, S., & Firdaus-Raih, M. (2015). Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient. Computational Biology and Chemistry, 59, 3–14.
Mu, Y., Liu, X., & Wang, L. (2018). A Pearson’s correlation coefficient based decision tree and its parallel implementation. Information Sciences, 435, 40–58.
Ouyang, Z. H., Qi, Q. X., Zhao, S. K., Wu, B. Y., & Zhang, N. B. (2015). The mechanism and application of deep-hole precracking blasting on rockburst prevention. Shock and Vibration, 2015, 1–7.
Pu, Y. Y., Apel, D. B., & Lingga, B. (2018). Rockburst prediction in kimberlite using decision tree with incomplete data. Journal of Sustainable Mining, 17(3), 158–165.
Pu, Y. Y., Apel, D. B., Liu, V., & Mitri, H. (2019a). Machine learning methods for rockburst prediction-state-of-the-art review. International Journal of Mining Science and Technology, 29(4), 565–570.
Pu, Y. Y., Apel, D. B., & Xu, H. W. (2019b). Rockburst prediction in kimberlite with unsupervised learning method and support vector classifier. Tunnelling and Underground Space Technology, 90, 12–18.
Roohollah, S. F., & Abbas, T. (2019). Long-term prediction of rockburst hazard in deep underground openings using three robust data mining techniques. Engineering with Computers, 35(2), 659–675.
Russenes, B. (1974). Analyses of rockburst in tunnels in valley sides. Trondheim: Norwegian Institute of Technology.
Salunkhe, U. R., & Mali, S. N. (2016). Classifier ensemble design for imbalanced data classification: A hybrid approach. Procedia Computer Science, 85, 725–732.
Shi, Q., Pan, Y. S., & Li, Y. J. (2005). The typical cases and analysis of rockburst in China. Coal Mining Technology, 2, 13–17.
Sousa, L. R., Miranda, T., Sousa, R. L., & Tinoco, J. (2017). The use of data mining techniques in rockburst risk assessment. Engineering, 3(4), 552–558.
Sun, Y., Li, G., & Zhang, J. (2020a). Developing hybrid machine learning models for estimating the unconfined compressive strength of jet grouting composite: A comparative study. Applied Sciences, 10(5), 1612.
Sun, Y., Li, G., Zhang, N., Chang, Q., Xu, J., & Zhang, J. (2020b). Development of ensemble learning models to evaluate the strength of coal-grout materials. International Journal of Mining Science and Technology. https://doi.org/10.1016/j.ijmst.2020.09.002.
Sun, Y., Li, G., Zhang, J., & Qian, D. (2019a). Prediction of the strength of rubberized concrete by an evolved random forest model. Advances in Civil Engineering, 2019(3), 1–7.
Sun, J., Wang, L. G., Zhang, H. L., & Shen, Y. F. (2009). Application of fuzzy neural network in predicting the risk of rock burst. Procedia Earth and Planetary Science, 1(1), 536–543.
Sun, Y., Zhang, J., Li, G., Ma, G., Huang, Y., Sun, J., et al. (2019b). Determination of Young’s modulus of jet grouted coalcretes using an intelligent model. Engineering Geology, 252, 43–53.
Sun, Y., Zhang, J., Li, G., Wang, Y., Sun, J., & Jiang, C. (2019c). Optimized neural network using beetle antennae search for predicting the unconfined compressive strength of jet grouting coalcretes. International Journal for Numerical and Analytical Methods in Geomechanics. https://doi.org/10.1002/nag.2891.
Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.
Wu, S., Wu, Z., & Zhang, C. (2019). Rock burst prediction probability model based on case analysis. Tunnelling and Underground Space Technology, 93, 103069.
Xu, C., Liu, X. L., Wang, E. Z., Zheng, Y. L., & Wang, S. J. (2018). Rockburst prediction and classification based on the ideal-point method of information theory. Tunnelling and Underground Space Technology, 81, 382–390.
Xue, Y., Bai, C., Qiu, D., Kong, F., & Li, Z. (2020). Predicting rockburst with database using particle swarm optimization and extreme learning machine. Tunnelling and Underground Space Technology, 98, 103287.
Zhang, Q., Wang, E., Feng, X., Niu, Y., Ali, M., Lin, S., et al. (2020a). Rockburst risk analysis during high-hard roof breaking in deep mines. Natural Resources Research, 29, 4085–4101.
Zhang, J., Wang, Y., Sun, Y., & Li, G. (2020b). Strength of ensemble learning in multiclass classification of rockburst intensity. International Journal for Numerical and Analytical Methods in Geomechanics. https://doi.org/10.1002/nag.3111.
Zhou, J., Koopialipoor, M., Li, E., & Armaghani, D. J. (2020). Prediction of rockburst risk in underground projects developing a neuro-bee intelligent system. Bulletin of Engineering Geology and the Environment, 79, 4265–4279.
Zhou, J., Li, X. B., & Mitri, H. S. (2016a). Classification of rockburst in underground projects: Comparison of ten supervised learning methods. Journal of Computing in Civil Engineering, 30(5), 04016003.
Zhou, J., Li, X. B., & Mitri, H. S. (2018). Evaluation method of rockburst: State-of-the-art literature review. Tunnelling and Underground Space Technology, 81, 632–659.
Zhou, J., Li, X. B., & Shi, X. Z. (2012). Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines. Safety Science, 50(4), 629–644.
Zhou, K. P., Lin, Y., Deng, H. W., Li, J. L., & Liu, C. J. (2016b). Prediction of rock burst classification using cloud model with entropy weight. Transactions of Nonferrous Metals Society of China, 26(7), 1995–2002.
Acknowledgments
This research is supported by National Natural Science Foundation of China under Grant Nos. 41941018 and 41807250, China Postdoctoral Science Foundation Program under Grant Nos. 2019T120686, and National Key Basic Research Program of China under Grant Nos. 2015CB058102. These supports are gratefully acknowledged.
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Yin, X., Liu, Q., Pan, Y. et al. Strength of Stacking Technique of Ensemble Learning in Rockburst Prediction with Imbalanced Data: Comparison of Eight Single and Ensemble Models. Nat Resour Res 30, 1795–1815 (2021). https://doi.org/10.1007/s11053-020-09787-0
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DOI: https://doi.org/10.1007/s11053-020-09787-0