Identification of water sources of mine water bursts based on the FPS-DT model

Abstract

Abstract To effectively identify the source of water in coal mines and prevent water-related accidents, this paper utilises the hydrochemical characteristics of the aquifers Shanxi Hanzui Coal Mine. The fuzzy C-means (FCM) clustering method is employed to classify water sample data, followed by principal component analysis (PCA) for dimensionality reduction to extract key features. The SMOTE algorithm is then applied to address the issue of class imbalance. Based on this, a decision tree model (FPS-DT) is constructed using the CART algorithm. To validate the model’s performance, five-fold cross-validation was used for evaluation. The results showed that the average classification accuracy of the FPS-DT model was 93%. In contrast, the accuracy of the comparison model, which only used PCA and decision trees, was 78%, indicating that the method proposed in this paper has significant advantages in terms of identification accuracy and generalisation capability. Additionally, the FPS-DT model features a clear structure and explicit classification rules, offering good interpretability and robustness. It can adapt to the real-time water source identification requirements of complex underground environments, providing theoretical support and technical assurance for coal mine safety production and water hazard prevention and control.