摘要
鉴于矿井涌水威胁煤矿安全生产及其影响因素的复杂性,提出基于BP神经网络的矿井涌水量预测方法。在充分分析新安煤矿+25m开采水平的涌水影响因素的基础上,选取大气降水、采空区面积和底板构造断裂和采动裂隙三个影响因子,建立了非线性人工神经网络预测模型,对+25m开采水平的正常涌水量进行了预计。其结果和实际观测数据能够较好地相吻合,表明采用人工神经网络预计矿井涌水量是可行的。
Based on the fact that mine shaft inrush water threatens mine safety in production and the complex nature of influencing factors, a method of BP neural network was put forward for mine inrush water prediction. On the basis of ample analyses of influencing factors, the ANN input predictor variables were precipitation, gob area size, floor and mining caused fractures. Elementary theory, composition and method of BP model were expounded, and a prediction model of non-linear artificial BP neural network was established. Proceeded from actual production condition （characteristic of inrush water） of ＋25m level in Xinan mine, the quantity of normal inrush water was predicted. The data demonstrated that the prediction was identical with observed data. It means to use the BP neural network in mine water inrush prediction is feasible.
出处
《中国煤田地质》
2007年第2期38-40,共3页
Coal Geology of China
