# Investigation on Intelligent Early Warning of Rock Burst Disasters Using the PCA-PSO-ELM Model
###### Abstract  
In order to conduct an intelligent early warning assessment of stope rock burst disasters in mining areas, and effectively prevent and control them, the principal component analysis (PCA) method was embraced to perform dimensionality reduction and feature information extraction from 10 main factors that affect the occurrence of rock bursts. On this basis, six principal component elements of the influencing factors of rock bursts have been obtained as the input vectors for an extreme learning machine (ELM). In the meantime, the parameter optimization ability of the PSO algorithm was adopted, the input weight values of the ELM and the threshold values of the hidden layer were optimized, and the functions of the three models were completely combined. Therefore, an early warning model of rock bursts based on the PCA-PSO-ELM combined algorithm was creatively proposed and the risk rank of rock bursts in the Yanshtiai Coal Mine was predicted and evaluated. Consequently, the research results indicated that the prediction accuracy of the PCA-PSO-ELM model improved the prediction performance and generalization ability and reached a 100% contrast with the three models, namely the BP neural network, the radial basis function, and the extreme learning machine, which presented an updated method for the early warning investigation of rock burst disasters and had favorable engineering significance. rock burst; principal component analysis (PCA) method; particle swarm optimization (PSO) algorithm; extreme learning machine (ELM) +
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difficulty in determining critical indicators [14]. In addition, the early warning assessment of rock bursts using numerical simulation is primarily based on the theoretical knowledge of elastoplastic mechanics, fracture mechanics, and damage theory. Afterwards, some complex cases that cannot explicitly express the analytical solution in theory are solved using numerical methods, and the mechanical response of surrounding rock under the influences of mining has been calculated [15]. Physical and mathematical models regarding the occurrence of rock bursts in mines, such as those presented by [PERSON] [16], were employed to investigate the mechanisms of occurrence for rock bursts using the PFC software. [PERSON] et al. [17] evaluated the critical depth of rock bursts at different structural levels using extensive numerical simulations in light of the discontinuous deformation analysis (DDA) method. Since the occurrence processes of rock bursts can be analyzed using numerical simulation methods from the point of view of the mechanism, it is impossible to veritably simulate a rock burst in the mining environment with respect to the complexity and uncertainty of the stop rock mass. In other words, the simulation results often present relatively considerable errors in on-site situations. Therefore, inaccurate early warning cases would occur. Moreover, the uncertainty theories mainly include fuzzy mathematics [18; 19], the rough set theory [20; 21], the cloud model theory [22; 23], the analytic hierarchy process (AHP) [24; 25], the grey system theory [26], and the set pair analysis [27], which have been broadly applied to the prediction study of rock bursts. Although these methods introduce fuzzy and random characteristics to the influencing factors of rock bursts [28], the evaluation and analysis of the rock burst indicators mainly rely on experience and are relatively subjective, leading to a large gap between the prediction results and the actual situation. Furthermore, intelligent algorithms essentially utilize enormous nonlinear capabilities, such as machine learning, to evaluate and provide early warnings of rock bursts. [PERSON] et al. [29] established an intelligent evaluation model for the risk of rock bursts in coal seams based on the BP neural network, which has been adequately validated in engineering. [PERSON] et al.