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中文摘要：
文章分析了孔隙充水矿井的充水水源和通道,利用非线性的BP人工神经网络建立了徐州韩桥煤矿涌水量短期预测模型,选取每天的降水量作为影响因子,用已有的涌水量资料训练得到权值和阈值来表示充水通道,并对-200m水平、-270m水平、-330m水平和全矿井涌水量进行了预测。结果显示,涌水量的预测值与实测值吻合得较好,说明该模型具有一定实用性。
英文摘要：
In this paper, sources and channels of water bursting of mine with pore water yield were analyzed and basic theory of artificial neural network was used. The short-time prediction model of mine inrush in the Hanqiao colliery was also established. Daily precipitation within a period of time was chosen as an influence factor. Weight and threshold, which were obtained from training known data of precipitation, were expressed as channels of water inrush. The mine inrush water of - 200 m level, - 270 m level, - 330 m level and the whole mine was predicted. The results show that it is right and feasible to build the BP neural network model and predict mine inrush water.
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