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Performance Evaluation of Rockburst Prediction Based on PSO-SVM, HHO-SVM, and MFO-SVM Hybrid Models

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Abstract

Rockburst prediction is crucial in deep hard rock mines and tunnels to make safer working conditions. Due to the complex interaction of many factors involved in rockburst prediction, such as multi-variable and multi-interference factors, three hybrid support vector machine (SVM) models optimized by particle swarm optimization (PSO), Harris hawk optimization (HHO), and moth flame optimization (MFO) are proposed to predict rockburst hazard level (RHL). The RHL is determined according to four kinds of microseismic characteristic parameters including angular frequency ratio, total energy, apparent stress, and convexity radius. Then, six types of microseismic characteristic parameters are taken as input variables in 343 sets of data, including angular frequency ratio and total energy, etc.. And the RHL is taken as the output target of rockburst prediction. The classification performance of PSO-SVM, HHO-SVM, and MFO-SVM hybrid models is evaluated by accuracy (ACC), precision (PRE), and kappa coefficient. Findings reveal that the MFO-SVM model performs best in terms of accuracy, with ACC, PRE, and kappa coefficients reaching 0.9559, 0.9063, and 0.9094 respectively, while PSO-SVM and HHO-SVM have similar performances. However, the PSO-SVM, HHO-SVM, and MFO-SVM all perform better than the unoptimized SVM model. This confirms that the three optimization algorithms significantly enhance the rockburst prediction capacity of the SVM model to help mine practitioners apply machine learning methods to rockburst prediction problems appropriately.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Funding

This research was funded by the National Science Foundation of China (42177164 and 41807259), the Distinguished Youth Science Foundation of Hunan Province of China (2022JJ10073), and the Innovation-Driven Project of Central South University (2020CX040).

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Authors and Affiliations

  1. School of Resources and Safety Engineering, Central South University, Changsha, 410083, China

    Jian Zhou, Peixi Yang, **an Peng & Yingui Qiu

  2. Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, VIC 3350, Australia

    Manoj Khandelwal

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  1. Jian Zhou
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  2. Peixi Yang
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Correspondence to Jian Zhou or **an Peng.

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Highlights

• Support vector machine (SVM) with PSO, HHO, and MFO for rockburst prediction modeling.

• ACC, PRE, kappa coefficients, and confusion matrix are used to compare the effect of hybrid rockburst prediction models.

• MFO-SVM hybrid model has the best effect on rockburst prediction.

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Zhou, J., Yang, P., Peng, P. et al. Performance Evaluation of Rockburst Prediction Based on PSO-SVM, HHO-SVM, and MFO-SVM Hybrid Models. Mining, Metallurgy & Exploration 40, 617–635 (2023). https://doi.org/10.1007/s42461-022-00713-x

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