Coal mine floor water inrush prediction based on CNN neural network

With a view to improving the accuracy of water bursting prediction in coal seam floor, a model based on Convolutional Neural Network (CNN) was established. Through comprehensive analysis of water bursting in coal seam floor, 15 factors affecting water bursting prediction were determined and these factors were combined to stimulate the partial correlation among these factors. These factors and their interrelation on water bursting prediction in coal seam floor were simulated by using the structure model established for depth calculation. Training and prediction were performed by using the known 115 sets of data. To verify the model efficiency, the BP neural network model and the LeNet-5 model were trained by using the same data, and then the established BP neural network model was compared with the LeNet-5 model. The result indicates that the interrelation between factors affecting water bursting prediction is considered comprehensively by deepening the calculation depth of the model, and the accuracy of water bursting prediction is improved. The prediction model of water bursting prediction in coal seam floor based on Convolutional Neural Network (CNN) has high accuracy and small standard error, which improves the accuracy of prediction effectively.