Toward Precise Long-Term Rockburst Forecasting: A Fusion of SVM and Cutting-Edge Meta-heuristic Algorithms

• Published in: Natural resources research (New York, N.Y.) Vol. 33; no. 5; pp. 2037 - 2062
• Main Authors: Armaghani, Danial Jahed, Yang, Peixi, He, Xuzhen, Pradhan, Biswajeet, Zhou, Jian, Sheng, Daichao
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2024; Springer Nature B.V
• Subjects: Accuracy; Algorithms; brittleness; Chemistry and Earth Sciences; compression strength; Compressive properties; Compressive strength; Computer Science; data collection; Earth and Environmental Science; Earth Sciences; Effectiveness; energy; Forecasting; Fossil Fuels (incl. Carbon Capture); Geography; Geological hazards; Heuristic methods; hybrids; Machine learning; Mathematical Modeling and Industrial Mathematics; Mineral Resources; Optimization; Optimization algorithms; Original Paper; Pandion haliaetus; Parameters; Performance evaluation; Performance prediction; Physics; prediction; Problem solving; Recall; Rime; Rockbursts; Rocks; Statistics for Engineering; Support vector machines; Sustainable Development; tensile strength; Uniaxial tensile strength; Rime-ice optimization algorithm; Osprey optimization algorithm; LIME; Dingo optimization algorithm; Long-term rockburst
• ISSN: 1520-7439; 1573-8981
• DOI: 10.1007/s11053-024-10371-z

Rockburst is one of the most hazardous geological disasters in underground engineering due to its complex causes and destructive nature. To address this, there is an imperative for methodologies that can predict rockbursts quickly and effectively to mitigate preemptively the risks and damages. In this study, 259 rockburst instances were analyzed, employing six rockburst feature parameters: maximum tangential stress ( σ θ ), uniaxial compressive strength of rock ( σ c ), uniaxial tensile strength of rock ( σ t ), stress coefficient ( σ θ /σ t ), rock brittleness coefficient ( σ c /σ t ), and elastic energy index ( Wet ) as inputs. By integrating three novel meta-heuristic algorithms—dingo optimization algorithm (DOA), osprey optimization algorithm (OOA), and rime-ice optimization algorithm (RIME)—with support vector machine (SVM), hybrid models for long-term rockburst trend prediction were constructed. Performance evaluations through fivefold cross-validation revealed that for the no rockbursts, DOA–SVM (Pop = 200) demonstrated superior predictive performance, achieving an accuracy of 0.9808, precision of 0.9231, recall of 1, and an F1-score of 0.96. For moderate rockbursts, OOA–SVM (Pop = 100) emerged as the most effective, registering an accuracy of 0.9808, precision of 0.9545, recall of 1, and an F1-score of 0.9767. For light and severe rockbursts, DOA–SVM, OOA–SVM, and RIME–SVM showcased comparable predictive outcomes. However, these hybrid models outperformed traditional SVM models optimized with conventional algorithms in terms of accuracy across all rockburst hazard levels. Moreover, the hybrid models underwent additional validation with a new dataset of 20 rockburst instances collected globally, confirming their robust efficacy and exceptional generalization capabilities. An ensuing analysis using local interpretable model-agnostic explanations (LIME) on the six key feature parameters revealed a significant positive correlation between σ θ and Wet with the severity of rockbursts. These results not only affirm the superior optimization performance of the DOA, OOA, and RIME algorithms but also their substantial potential to enhance the predictive accuracy of machine learning models in forecasting long-term rockbursts.