基于CNN神经网络的煤层底板突水预测

为了提高煤层底板突水预测的准确性，建立了基于卷积神经网络的煤层底板突水预测模型。通过综合分析，确定了15个影响煤层底板突水的因素，将这些影响因素进行拼接组合，运用建立的深度计算结构模型对影响因素及其相互联系进行特征提取。用已知的115组数据对模型进行学习训练，并进行了预测。为验证模型的准确性，利用相同的数据对BP神经网络模型和LeNet-5模型进行训练，将建立的模型与BP神经网络模型和LeNet-5模型进行对比。结果表明:该模型通过加深模型的计算深度，综合考虑了影响底板突水因素间的相互联系，提高了突水预测准确性。基于卷积神经网络构建的模型可以对煤层底板突水进行预测，并且准确率相对较高。

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.