Discrimination Method for Water Inrush Source of Mine Based on Rough Sets Theory and BP Neural Network

被引：0

作者：

Yan Zhen ^{[1
]}

Qian Jiazhong ^{[1
]}

Zhao Weidong ^{[1
]}

机构：

[1] Hefei Univ Technol, Sch Resources & Environm Engn, Hefei 230009, Peoples R China

来源：

PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON ENVIRONMENTAL TECHNOLOGY AND KNOWLEDGE TRANSFER | 2012年

基金：

中国国家自然科学基金;

关键词：

rough sets; BP neural network; water inrush source discrimination; Panyi mine;

D O I：

暂无

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Based on rough set theory and BP neural network theory, discriminating method for water inrush source of mine was studied. The chemical indicators of inrush water were chosen to constitute a water sample matrix. First, the rough sets theory was applied to sample information reduction, then the BP neural network was applied to water source discrimination. Discriminating model for water source was established based on rough set theory and BP neural network, and compared with the traditional BP neural network model. Taked Panyi mine in Huainan for example, the results are compared with those of BP neural network model and show that the discriminating method based on rough set theory and BP neural network theory had a higher discrimination accuracy (92.5%)than the BP neural network method(82.5%). The principle of the method was clear and applied easily.

引用

页码：821 / 825

页数：5

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