An Unsupervised Learning Approach for Coal Spontaneous Combustion Warning Level Classification Using t-SNE and k-Means Clustering

Accurate prediction of coal spontaneous combustion levels is crucial for preventing and controlling spontaneous combustion in goaf areas. To address the ambiguity in classification standards of coal spontaneous combustion warning levels, 21 groups of coal samples from different mining areas were subjected to experiments with programmed temperatures, generating a database of 336 sets of temperatures and data on indicator gas concentrations. An unsupervised learning approach combining t-distributed Stochastic Neighbor Embedding (t-SNE) and k-means clustering was proposed to perform dimensionality reduction and clustering of high-dimensional data features. The clustering results of the original data were compared with Principal Component Analysis (PCA) and Stochastic Neighbor Embedding (SNE) methods to determine coal spontaneous combustion warning levels. The indicator gases and warning levels were input into a trained Support Vector Classifier (SVC) to establish a classification model for coal spontaneous combustion warning levels in goaf areas. The results showed that the maximum Maximal Information Coefficients (MICs) between temperature and CO and O2 concentrations were 0.95 and 0.81, respectively, indicating strong nonlinear relationships between indicator gases and warning levels. The t-SNE method effectively extracted nonlinear mapping relationships between the indicator gas features, while the k-means clustering categorized coal spontaneous combustion data using distance as a similarity measure. By combining the t-SNE and k-means methods for accurate dimensionality reduction and clustering of goaf spontaneous combustion data, the warning levels were classified into six categories: safe, low risk, moderate risk, high risk, severe risk, and extremely severe risk. The application in the Longgu mine demonstrated that the SVC method could accurately classify spontaneous combustion warning levels in field goaf areas and implement corresponding response measures based on different warning levels, providing a valuable reference for spontaneous combustion prevention in goaf areas.