Abstract:In order to obtain the regulation s of mining pressure and predict the location of the pressure on the working face accurately, a physical simulation test of similar materials was set up, and distributed optical fibers were used to monitor the deformation of the overlying rock during coal mining. Using gated recurrent neural networks, Combining the statistical characteristics of the frequency shift value of the optical fiber sensor and other factors affecting the rock pressure, such as the location of working face, the GA-GRU-BP position prediction model is established on working face. Using Genetic Algorithm (GA) to optimize the hyperparameters during the GRU network training. Correlation coefficient (R2), mean absolute error (MAE) and root mean square error (RMSE) are used to evaluate algorithm performance. GA-GRU-BP is compared with random forest (RF), GA-support vector machine(SVM), and GA-GRU. The experimental results show that the R2, MAE, and RMSE of the GA-GRU-BP prediction model are98.7%, 1.224cm, and 1.769cm, respectively, which are all lower than the evaluation indicators of the corresponding comparison method. The GA-GRU-BP position prediction model has a higher accuracy and robustness. A new method for predicting the position of the mining pressure is provided on working face.