| 基于BP神经网络方法的矿井涌水量预测 |
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| 引用本文： | 姜素,孙亚军,杨兰,凌成鹏.基于BP神经网络方法的矿井涌水量预测[J].中国煤田地质,2007,19(2):38-40. |
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| 作者姓名： | 姜素 孙亚军 杨兰 凌成鹏 |
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| 作者单位： | 中国矿业大学资源与地球科学学院,江苏徐州221008 |
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| 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.
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| 关 键 词： | 矿井涌水量 影响因素 预测模型 BP神经网络 新安煤矿 |
| 文章编号： | 1004-9177（2007）04-0038-03 |
| 收稿时间： | 2007-01-07 |
| 修稿时间： | 2007年1月7日 |
Mine Inrush Water Prediction Based on BP Neural Network Method |
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| Jiang Su, Sun Yajun, Yang Lan, Ling Chengpeng.Mine Inrush Water Prediction Based on BP Neural Network Method[J].Coal Geology of China,2007,19(2):38-40. |
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| Authors: | Jiang Su Sun Yajun Yang Lan Ling Chengpeng |
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| Institution: | College of Resources and Geosciences, CUMT, Xuzhou, Jiangsu 221008 |
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| Abstract: | 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 influenc- ing 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 estab- lished.Proceeded from actual production condition(characteristic of inrush water)of +25m level in Xinan mine,the quantity of nor- real 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. |
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| Keywords: | mine inflow influencing factors prediction model BP neural network Xinan Mine |
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