A Distributed Optical Fiber Monitoring Mine Pressure Prediction Model Based on Convolutional Neural Network

Abstract

In order to ensure the safe production of coal mines, this paper proposes a mine pressure prediction model based on convolutional neural network, which takes the optical fiber frequency shift data of the three-dimensional physical simulation test of similar materials to monitor the deformation of the overburden rock under mining as input, to predict the location of the next mine pressure on the working face. The BP neural network is used as a comparison algorithm, and the root mean square error (RMSE), average absolute error (MAE) and average absolute percentage error (MAPE) are used as the evaluation indicators of the mine pressure prediction model. The experimental results show that the RMSE, MAE and MAPE of the mine pressure prediction method based on the convolutional neural network proposed in this paper are lower than the BP neural network model, which has higher accuracy and robustness, and it provides a feasible scheme for realizing accurate mine pressure prediction.