• 责任者：Ling Chengpeng (China University of Mining and Technology ; Xuzhou 221008 ; China) ; Sun Yajun
• 刊名：Hydrogeology and Engineering Geology
• 出版年：2007
• 卷期：34(5)
• 关键词：矿坑涌水量预算 ; estimating water yield of mine
• 页码：p.55-58
• 图表：3 illus., 1 table, 11 refs.
• 分类号：1701
• issnNo：ISSN1000-3665
• isbnNo：CN11-2202/P

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.