CatBoost mine pressure appearance prediction based on Bayesian algorithm optimization

Obtaining mine pressure data through traditional monitoring methods and using statistical or machine learning algorithms to predict mine pressure can no longer meet the requirements of intelligent development in mines. It is necessary to seek new methods to improve the accuracy and real-time performance of mine pressure data monitoring and prediction. Based on three-dimensional similar physical model experiments, a distributed fiber optic monitoring system is constructed. The distributed fiber optic cables are pre-embedded along the model's direction and height. Pressure data is collected during the simulated mining process of the working face, and the optical fiber Brillouin frequency shift mean variation degree is introduced as an indicator to determine whether the pressure is coming. By preprocessing the optical fiber monitoring data such as noise removal, normalization and phase space reconstruction, the one-dimensional initial monitoring data is converted into three-dimensional data. The method uses Bayesian algorithm to iteratively optimize the parameters of the CatBoost algorithm. After reaching the maximum number of iterations, the optimal parameter combination is loaded into the CatBoost algorithm. The prediction model for mine pressure appearance is obtained by training. The results show that the Bayesian algorithm has fewer iterations and smaller errors than traditional grid search methods. Compared with random forest (RF), gradient boosting decision tree (GBDT) and extreme gradient boosting (XGBoost), the CatBoost algorithm has higher prediction accuracy and stronger generalization capability. The CatBoost mine pressure appearance prediction model optimized by the Bayesian algorithm can accurately predict the three weighting in the test set. The overall prediction trend is in line with the measured value, with mean absolute error of 0.0091, root-mean-square error of 0.0077, and determination coefficient of 0.933 9.