Risk level early warning for coal gas compound dynamic disasters via multi-source information fusion and deep learning

WANG Kai; LI Kangnan; DU Feng; ZHAO Wei; ZHAO Yu; ZHANG Junwen; ZHAO Minghao

doi:10.13225/j.cnki.jccs.2025.1208

Abstract：Under deep mining conditions,the failure mechanisms of coal gas compound dynamic disasters are highly com-plex and involve strong multi-factor coupling,so accurate early warning is critical for ensuring safe mine production.This study proposes a deep learning-based early-warning approach driven by multi-source information fusion and develops an SCSSA-MSDA-TFT time-series intelligent early-warning model.In this framework,an Sine-Cosine and Cauchy-en-hanced Sparrow Search Algorithm(SCSSA)is employed to adaptively optimize the model hyperparameters,Multi-Source Domain Adaptation(MSDA)is introduced to align the distributions of heterogeneous monitoring data and achieve unified feature representation,and a Temporal Fusion Transformer(TFT)is used to efficiently extract the dynamic evolutionary characteristics of multi-source time-series indicators,thereby enabling risk-level early warning.For multi-source informa-tion such as microseismic monitoring and gas-related parameters,a data-driven risk-level calibration procedure for coal gas compound dynamic disasters is constructed.Taking a Composite Risk Index(CRI)as the core,the CRI series is tem-porally smoothed,and the high-risk threshold is determined based on Receiver Operating Characteristic(ROC)curve ana-lysis.Cluster validity evaluation is then used to assess the consistency between the calibrated risk levels and the intrinsic data structure.Furthermore,a compound dynamic disaster early-warning indicator system is established:an XGBoost multi-class baseline model is trained and global Shapley Additive Explanations(SHAP)importance is computed,which,combined with sliding-window robustness checks and subset selection criteria,yields a compact indicator subset that bal-ances physical interpretability and discriminative efficiency.The results show that the proposed model achieves a macro-averaged Fl-score of 0.965 and an accuracy of 0.961 on the test set,significantly outperforming the comparison and abla-tion models.The model can accurately capture multi-scale precursory signals of coal gas compound dynamic disasters and realize precise prediction and early warning of risk levels.The proposed deep learning fusion early-warning approach ef-fectively integrates multi-source information and establishes a coherent risk-level calibration and indicator system,offer-ing substantial engineering application value for improving the accuracy and reliability of risk-level early warning for coal gas compound dynamic disasters.