Research on Multi-Source Collaborative Leakage Location Method for Coal Mine Gas Extraction Pipeline Based on Stacking Integration Learning

The accurate location of leakage points is a key part of underground gas prevention. To solve the problem of low positioning accuracy for gas extraction pipeline leakage, the gas extraction pipeline leakage experimental system was built, and the multi-source collaborative leakage localization method based on Stacking learning was proposed. The results showed that the Stacking–LSSVM–Elman–DBN (S-L-E-D) model with pressure–flow collaborative input achieved the best localization performance, with an accuracy of 0.932, Root Mean Square Error (RMSE) of 0.053, Mean Absolute Percentage Error (MAPE) of 0.082, Theil Inequality Coefficient (TIC) of 0.056, and a distance error below 1 m. Compared with a single-parameter input, the collaborative pressure–flow input improved the localization accuracy by more than 10%, while the RMSE and MAPE decreased by 39.0% and 37.4%, respectively. Under monitoring point fault conditions, the localization accuracies of monitoring points 1, 4, and 5 were 0.884, 0.891, and 0.881, respectively, while the dual-fault condition of monitoring points 1 and 4 still maintained an accuracy of 0.861. The study provides a feasible multi-source collaborative learning framework for leakage localization in gas extraction pipelines and offers a methodological reference for improving leakage monitoring and early warning.