Prediction of failure depth of coal seam floor in pressure mining based on small sample enhancement of MTD class distribution

The prediction method of machine learning has been widely used in the prediction and evaluation of the failure depth of coal seam floor in pressure mining. However, there are often some problems in the construction of the prediction model, such as high acquisition cost, difficulty in collecting and strong randomness of the measured data. The prediction performance of the model built based on a small number of samples is often severely restricted by the prediction accuracy and generalization ability. Through literature research, 50 sets of measured data samples were collected, and MTD similar distribution virtual sample generation technology was introduced to generate virtual samples to further expand and enhance the measured samples of coal seam floor failure depth.Machine learning algorithms such as ADE-ELM, GA-PSO-BP and BP were used to build a prediction model of coal seam floor failure depth before and after virtual sample enhancement, and the prediction accuracy of the model before and after enhancement was compared and analyzed. The results show that the distribution of virtual samples generated by this method is basically consistent with that of measured samples. The accuracy of the prediction models enhanced with virtual samples is significantly improved, among which the PCA-ADE-ELM prediction model enhanced with small distributed samples of MTD class has the best prediction effect, and the error of the enhanced model can be reduced by 42.95%～51.27%. MTD similar distribution virtual sample generation technology is used to enhance small samples, and the prediction model of failure depth of coal seam floor under pressure can be built to more accurately predict the failure depth of coal seam floor under the influence of multiple factors. Through comparison and analysis with the standard empirical prediction results and the slip line field theory prediction results, the failure depth of 19105 working face of Yunjialing Mine predicted by this method is relatively large, which is conducive to the safe production management of working face. The relevant research results provide favorable support for the safe and efficient mining of confined above-water coal seam of Ordovician limestone.