煤层底板采动导水破坏深度计算的神经网络方法

Prediction of the Failure Depth of Coal Seam Floor by Artificial Neural Network Method

在综合分析影响煤层底板采动导水破坏深度因素的基础上 ,应用人工神经网络方法 ,建立了底板破坏深度的计算模型。该模型利用现场观测资料作为学习训练样本和测试样本 ,对模型的测算结果、理论计算值和实测值进行了对比分析。结果表明 :用神经网络方法计算底板破坏深度考虑的因素更加全面 ,结果更接近于实际。笔者研究的计算模型和测算方法 ,为承压水上安全采煤决策提供了科学依据。

Based on the analysis of the factors influencing the failure depth of coal seam floor, a model to predict the failure depth is established by applying the theory of artificial neural network (ANN). A large amount of on-site observed data is used as learning and training samples. Then the predicted results from the model, theoretical results and the observed values are compared and analyzed. The results show that it is more precise to predict the failure depth of coal seam floor by ANN technology. It provides scientific criteria for deciding the mining of the coal over the confined aquifer.