Kadkhodaei Mohammad Hossein, Ghasemi Ebrahim
Department of Mining Engineering, Isfahan University of Technology, Isfahan, 8415683111, Iran.
Sci Rep. 2025 Jan 6;15(1):911. doi: 10.1038/s41598-024-85042-3.
In this study, two novel hybrid intelligent models were developed to evaluate the short-term rockburst using the random forest (RF) method and two meta-heuristic algorithms, whale optimization algorithm (WOA) and coati optimization algorithm (COA), for hyperparameter tuning. Real-time predictive models of this phenomenon were created using a database comprising 93 case histories, taking into account various microseismic parameters. The results indicated that the WOA achieved the highest overall performance in hyperparameter tuning for the RF model, outperforming the COA. RF-WOA model accurately predicted the occurrence of this phenomenon with an accuracy of 0.944. Additionally, for this model, precision, recall and F1-score were obtained as 0.950, 0.944 and 0.943, respectively, indicating that the proposed model is robust in predicting damage severity of rockburst in deep underground projects. Subsequently, the Shapley additive explanations (SHAP) method was employed to interpret and explain the prediction process and assess the influence of input features based on RF-WOA model. The results showed that three parameters including cumulative seismic energy, cumulative microseismic events, and cumulative apparent volume have the greatest impact on the occurrence of rockburst events. This study provides an interpretable and transparent resource for accurately predicting rockburst events in real time. It can facilitate estimating project costs, selecting a suitable support system, and identifying essential ways to limit the danger of rockburst.
在本研究中，开发了两种新型混合智能模型，使用随机森林（RF）方法以及两种元启发式算法——鲸鱼优化算法（WOA）和浣熊优化算法（COA）进行超参数调整，以评估短期岩爆。利用包含93个案例历史的数据库，考虑各种微震参数，创建了这种现象的实时预测模型。结果表明，在RF模型的超参数调整中，WOA取得了最高的整体性能，优于COA。RF-WOA模型以0.944的准确率准确预测了这种现象的发生。此外，对于该模型，精确率、召回率和F1分数分别为0.950、0.944和0.943，表明所提出的模型在预测深部地下工程岩爆破坏严重程度方面具有鲁棒性。随后，采用夏普利加法解释（SHAP）方法基于RF-WOA模型解释和说明预测过程，并评估输入特征的影响。结果表明，累积地震能量、累积微震事件和累积视体积这三个参数对岩爆事件的发生影响最大。本研究为实时准确预测岩爆事件提供了一种可解释且透明的资源。它有助于估算项目成本、选择合适的支护系统以及确定限制岩爆危险的关键方法。
