## 1 Introduction  
Rockburst is a critical threat to underground engineering, such as hydropower, mining, and transportation [1; 2; 3; 4]. As a sudden and destructive geologic disaster, rockburst has always been the subject of contentious and active research in the field of underground engineering [5]. It can not only cause project delays but even cause injuries and deaths [6]. Therefore, accurately predicting rockburst is crucial for belowground engineering, particularly in regions with significant in situ stress. However, due to the complex development mechanisms and various influencing factors [7; 8; 9; 10; 11; 12], it is still a huge challenge to predict rockburst accurately. Numerous models have been developed for rockburst prediction, which can be divided into conventional and intelligent models. For the former, it can be mainly classified into two types. The first one is single-factor methods [13; 14; 15], which can be classified into three types: strength-based, energy-based, and brittless-based methods [16]. Due to their simplicity and efficiency, the single-factor methods are widely utilized in practical engineering. However, the precision and reliability of single-factor methods are insufficient to tackle the diversity of rockburst influencing factors [17; 18]. The second one is multiple-factor methods, which can consider multiple influencing factors of rockburst by employing indicator weights and membership functions. The typical techniques include the multidimensional cloud model [19; 20], neural network [21; 22], fuzzy comprehensive evaluation method [23; 24; 25], extensible theory [26; 27], and TOPSIS [28; 29]. Although they have been widely applied, the indicator weights and membership functions are challenging to determine objectively. Recently, with the development of big data [30] and machine learning [31], more and more intelligent models of rockburst prediction have been developed. Intelligent models can build a complex and non-linear relationship between indicators and rockburst hazards based on existing cases. Compared to conventional models, the impact of subjective factors can be weakened, and the credibility of prediction results can be enhanced. For example, [PERSON] et al. [32] improved the naive Bayes algorithm's ability to predict rockburst by using four weight optimization techniques: forwards, backward, particle swarm optimization, and evolutionary optimization. [PERSON] et al. [33] assessed the rockburst hazard level in the Upper Silesian Coal Basin by using machine learning methods, such as neural networks, decision trees, random forests, gradient boosting, and extreme gradient boosting (XGBoost). [PERSON] and [PERSON] [34] suggested an interactive machine learning model predicting rockburst hazards at three levels. [PERSON] and [PERSON] [35] combined undersampling and oversampling methods with numerical modeling for small and imbalanced rockburst datasets. According to the BHP index, the clusters included in rockburst cases were replaced by representative synthetic cases obtained from numerical simulations. It should be noted that, in the early stage, only one single machine learning model was employed for the rockburst prediction. Subsequently, hybrid models combining multiple algorithms were employed to enhance prediction performance. For example, [PERSON] et al. [36] used extreme gradient boosting (XGBoost) to predict rockburst hazards in tunnels. [PERSON] et al. [37] integrated the XGBoost, light gradient boosting machine, and categorical boosting to assess rockburst risk based on microseismic data. By constructing six different intelligent models and evaluating them through seven indicators, [PERSON] et al. [38] proposed the Bayes-optimized convolutional neural network to predict rockburst hazards. [PERSON] and [PERSON] [39] investigated the applicability of the emotional neural network, gene expression programming, and decision tree-based C4.5 algorithm in predicting rockburst hazards under binary conditions. [PERSON] et al.