Mine pressure prediction method based on random forest

The analysis and prediction of mineral pressure manifestation in the coal mining process are of great significance to guarantee the safe and efficient production of coal mines. Distributed optical fiber monitoring technology has become a new method for monitoring overlying rock deformation during coal mining. This thesis takes the physical similarity simulation test of distributed optical fiber monitoring overburden deformation as the research background,and establishes the MBCT-SR-RF mineral pressure manifestation prediction model based on random forest. Firstly,the concept of fiber weighted frequency shift average change degree is defined,and introduce the multi-step backward cloud transform ( MBCT-SR ) to calculate the expected( Ex ),entropy( En ),and hyper-entropy( He ) of the frequency shift data of all measurement points on the fiber. And then, the fiber weighted frequency shift average change degree and the fiber frequency shift data statistical features ( Ex,En,He ) are used as the feature attributes of the input samples. Root mean square error ( RMSE ),average absolute error ( MAE ) and average absolute percentage error ( MAPE ) are used as performance evaluation metrics. The experimental results show that RMSE,MAE and MAPE are respectively 5.289 6 cm,4.336 7 cm and 3.916 7%. Compared with the BP neural network and the SVM support vector machine method,the prediction method of the pressure position of the working face based on MBCT-SR-RF has higher accuracy and robustness. This method realizes the mineral pressure manifestation using optical fiber frequency shift data. It provides a judgment basis for intelligent roof management during the mining process of the working face.