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.

岩爆因其成因复杂、破坏性强，是地下工程危害性最大的地质灾害之一。为了解决这个问题，迫切需要能够快速有效地预测岩爆的方法，以先发制人地减轻风险和损害。本研究对259个岩爆实例进行了分析，采用了6个岩爆特征参数：最大切向应力（σ _θ ）、岩石单轴抗压强度（σ _c ）、岩石单轴抗拉强度（σ _c ）。岩石（σ _t ）、应力系数（σ _θ /σ _t ）、岩石脆性系数（σ _c /σ _t ）和弹性能量指数（湿）作为输入。通过将三种新颖的元启发式算法——野狗优化算法（DOA）、鱼鹰优化算法（OOA）和雾凇优化算法（RIME）——与支持向量机（SVM）相结合，建立了用于长期岩爆趋势预测的混合模型被建造。通过五重交叉验证进行的性能评估表明，对于无岩爆的情况，DOA-SVM (Pop = 200) 表现出卓越的预测性能，准确度为 0.9808，精确度为 0.9231，召回率为 1，F1 分数为 0.96。对于中度岩爆，OOA-SVM (Pop = 100) 是最有效的，准确度为 0.9808，精确度为 0.9545，召回率为 1，F1 分数为 0.9767。对于轻度和重度岩爆，DOA-SVM、OOA-SVM 和 RIME-SVM 显示了类似的预测结果。然而，这些混合模型在所有岩爆危险级别的准确性方面优于使用传统算法优化的传统 SVM 模型。 此外，混合模型还使用全球收集的 20 个岩爆实例的新数据集进行了额外验证，证实了其强大的功效和卓越的泛化能力。随后对六个关键特征参数使用局部可解释模型不可知解释 (LIME) 进行的分析揭示了 σ _θ 和 Wet 与岩爆严重程度之间存在显着的正相关性。这些结果不仅证实了 DOA、OOA 和 RIME 算法的卓越优化性能，而且还证实了它们在提高机器学习模型在预测长期岩爆方面的预测准确性的巨大潜力。