首页 | 本学科首页   官方微博 | 高级检索  
     

基于BP神经网络的孔隙充水矿井涌水量预测
引用本文:凌成鹏, 孙亚军, 杨兰和, 姜素, 邵飞燕. 基于BP神经网络的孔隙充水矿井涌水量预测[J]. 水文地质工程地质, 2007, (5): 55-58. doi: 10.3969/j.issn.1000-3665.2007.05.014
作者姓名:凌成鹏  孙亚军  杨兰和  姜素  邵飞燕
作者单位:中国矿业大学,徐州,221008;; 中国矿业大学
基金项目:国家自然科学基金;国家重点基础研究发展计划(973计划)
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.

关 键 词:BP人工神经网络   孔隙充水矿井   涌水量   预测模型   韩桥煤矿
文章编号:1000-3665(2007)05-0055-04
修稿时间:2006-11-08

Prediction of inrush water of mine with pore water yield based on BP artificial neural network
LING Cheng-peng, SUN Ya-jun, YANG Lan-he, JIANG Su, SHAO Fei-yan. Prediction of inrush water of mine with pore water yield based on BP artificial neural network[J]. Hydrogeology & Engineering Geology, 2007, (5): 55-58. doi: 10.3969/j.issn.1000-3665.2007.05.014
Authors:LING Cheng-peng  SUN Ya-jun  YANG Lan-he  JIANG Su  SHAO Fei-yan
Affiliation:China University of Mining and Technology, Xuzhou 221008, China
Abstract: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.
Keywords:BP artificial neural network  mine with pore water yield  mine inrush water  prediction model  Hanqiao colliery
本文献已被 CNKI 维普 万方数据 等数据库收录!
点击此处可从《水文地质工程地质》浏览原始摘要信息
点击此处可从《水文地质工程地质》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号