A new approach to predicting mining induced surface subsidence

被引:9
|
作者
Ding De-xin [1 ]
Zhang Zhi-jun
Bi Zhong-wei
机构
[1] Cent S Univ, Sch Resources & Safety Engn, Changsha 410083, Peoples R China
[2] Nanhua Univ, Sch Architectural Resources & Environm Engn, Hengyang 421001, Peoples R China
来源
基金
中国国家自然科学基金;
关键词
mining induced surface subsidence; fuzziness and interaction of parameters; artificial neural fuzzy inference system;
D O I
10.1007/s11771-006-0064-y
中图分类号
TF [冶金工业];
学科分类号
0806 ;
摘要
There are many parameters influencing mining induced surface subsidence. These parameters usually interact with one another and some of them have the characteristic of fuzziness. Current approaches to predicting the subsidence cannot take into account of such interactions and fuzziness. In order to overcome this disadvantage, many mining induced surface subsidence cases were accumulated, and an artificial neuro fuzzy inference system(ANFIS) was used to set up 4 ANFIS models to predict the rise angle, dip angle, center angle and the maximum subsidence, respectively. The fitting and generalization prediction capabilities of the models were tested. The test results show that the models have very good fitting and generalization prediction capabilities and the approach can be applied to predict the mining induced surface subsidence.
引用
收藏
页码:438 / 444
页数:7
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