By applying the Wavelet Relevance Vector Machine (WRVM) method, this research proposes the loose zone of roadway surrounding rock prediction. Based on the theory of relevance vector machine (RVM), the wavelet function is introduced to replace the original Gauss function as the model kernel function to form the WRVM. Five factors affecting the loose zone of roadway surrounding rock are selected as the model input, and the prediction model of the loose zone of roadway surrounding rock based on WRVM is established. By using cross-validation method, the kernel parameters of three kinds of wavelet relevance vector machines (RVMs) are calculated. By comparing and analyzing the root mean square (RMS) error of the test results of each predictive model, the advantages and accuracy of the model are verified. In practical engineering applications, the average relative prediction errors of the Mexican relevance vector machine, the Morlet relevance vector machine and the difference of Gaussian (DOG) relevance vector machine models are accordingly 4.581%, 4.586% and 4.575%. The square correlation coefficient of the predicted samples is 0.95 > 0.9, which further verifies the accuracy and reliability of the proposed method.

To combat the uncertainty of the multiple factors affecting roadway surrounding rock stability, five initial indexes are selected for reduction according to concept lattice theory: rock quality designation (RQD), uniaxial compressive strength (Rc), the integrity coefficient of rock mass, groundwater seepage, and joint condition. The aim of this study is to compute correlation coefficients among various indexes and verify the effectiveness of lattice reduction. Alpha stable distribution is used to replace the commonly used Gauss distribution in probabilistic neural networks. A prediction model for the stability of roadway surrounding rock is then established based on a concept lattice and improved probabilistic neural network. 100 groups of training sample data are plugged into this model one by one to examine its rationality. The established model is employed for engineering application prediction with ten indiscriminate sample groups from the Jianlinshan mining area of the Daye iron mine, revealing accuracy of up to 90%. This demonstrates that our prediction model based on a concept lattice and improved probabilistic neural network has high reliability and applicability.

In order to accurately analyze the interaction among the risk factors of university emergencies, firstly, based on the key performance indicator (KPI) concept, by initially extracting risk factors, identifying initial risk factors, and correcting initial risk factors, and the risk factors of emergencies in universities were divided into three categories: human factors, environmental factors, and management factors, and 18 key risk factors of emergencies in universities, including psychological problems, weak legal awareness and lack of safety awareness, were subdivided and determined. Secondly, the interpretive structure model (ISM) was used to construct a multi-layer hierarchical model of the relationship between university emergencies risk factors, clarified the hierarchical affiliation between the risk factors of university emergencies, divided the risk factors of university emergencies into surface direct factors, middle indirect factors and deep fundamental factors, and analyzed the coupling effect of various risk factors on university emergencies. Thirdly, combined with the matrix impacts cross-reference multiplication applied to a classification (MICMAC), the driving force and dependence force of each risk factor of university emergencies were obtained, and the risk factors of university emergencies were divided into spontaneous factors, independent factors, dependent factors, linkage factors, etc. 4 categories, and targeted governance and prevention measures were proposed for different types of risk factors. Finally, the research results will help universities to accurately identify, warn and manage the key risks of emergencies, and provide a reference for the management of emergencies in universities.