Intelligent Prediction and Prevention of Coal Mine Water Inrush: Integrating Hybrid Data Augmentation, HO-SVR, and RAG-LLM Technologies

This study proposes a novel integrated framework that combines a Hippopotamus-Optimized Support Vector Regression (HO-SVR) prediction model with a Retrieval-Augmented Generation-enhanced Large Language Model (RAG-LLM)-based intelligent decision module, addressing the core challenge of bridging prediction and prevention in coal mine water inrush disasters. It represents the first application of the combined HO-SVR and RAG-LLM approach in this field. Methodologically, a hybrid data augmentation technique (SMOTE–GN–Bootstrap) alleviates data scarcity and imbalance, while feature selection and dimensionality reduction optimize the input features. The developed HO-SVR model demonstrates superior prediction accuracy over benchmark models. The key innovation lies in the RAG-LLM module which automatically generates interpretable reports and actionable prevention strategies based on the prediction results and key influencing factors, thereby establishing a closed-loop intelligent system from accurate prediction to informed prevention. Practically, this framework enables proactive risk management through data-driven predictions, significantly reduces water inrush incidents, and provides intelligent decision support for field operations, substantially enhancing mine safety. Furthermore, the study discusses the model’s potential and challenges across different geological settings, charting a course for developing more generalized models