Application research of SSA-RF model in predicting the height of water-conducting fracture zone in deep and thick coal seams

Abstract

The 91 measured values of the development height of the water-conducting fracture zone (WCFZ) in deep and thick coal seam mining faces under thick loose layer conditions were collected. Five key characteristic variables influencing the WCFZ height were identified. After removing outliers from the dataset, a Random Forest (RF) regression model optimized by the Sparrow Search Algorithm (SSA) was constructed. The hyperparameters of the RF model were iteratively optimized by minimizing the Out-of-Bag (OOB) error, resulting in the rapid determination of optimal parameters. Specifically, the SSA-RF model achieved an OOB error of 0.148, with 20 decision trees, a maximum depth of 8, a minimum split sample size of 2, and a minimum leaf node sample size of 1. Cross-validation experiments were performed using the trained optimal model and compared against other prediction methods. The results showed that the mining height had the most significant correlation with the development height of the WCFZ. The SSA-RF model outperformed all other models, with R² values exceeding 0.9 across the training, validation, and test datasets. Compared to other models, the SSA-RF model demonstrates a simpler structure, stronger fitting capacity, higher predictive accuracy, and superior stability and generalization ability. It also exhibits the smallest variation in relative error across datasets, indicating excellent adaptability to different data conditions.Furthermore, a numerical model was developed using the hydrogeological data from the 1305 working face at Wanfukou Coal Mine, Shandong Province, China, to simulate the dynamic development of the WCFZ during mining. The SSA-RF model predicted the WCFZ height to be 69.7 m, closely aligning with the PFC2D simulation result of 65 m, with an error of less than 5 %. Compared to traditional methods and numerical simulations, the SSA-RF model provides more accurate predictions, showing only a 7.23 % deviation from the PFC2D simulation, while traditional empirical formulas yield deviations as large as 19.97 %. These results demonstrate the SSA-RF model's superior predictive capability, reinforcing its reliability and engineering applicability for real-world mining operations. This model holds significant potential for enhancing mining safety and optimizing planning processes, offering a more accurate and efficient approach for WCFZ height prediction.