摘要
基于矿井涌水量序列具有明显的随机性和灰色特征,建立涌水量GM(1,2)预测模型,以克服灰色GM(1,1)模型对于随机波动大的长序列预测效果差的缺点。以王行庄煤矿为例,针对2012年7月至2013年12月王行庄煤矿18个月的涌水量资料,考虑与之密切相关的L7-8灰岩含水层水位降深,建立了矿井涌水量GM(1,2)预测模型;预测了2014年1—4月的涌水量;并与GM(1,1)预测模型进行模型精度与预测精度的比较。结果表明:GM(1,2)模型的预测精度达到了97.44%,GM(1,1)模型的预测精度为92.60%,GM(1,2)模型明显提高了矿井涌水量的预测精度。
Because mine discharge sequence has both randomness and gray characteristics obviously, a GM（1 ,2） prediction model was established to overcome shortcomings that the prediction effect of gray model was poorfor long time series of large random fluctuations. Firstly,mine discharge sequences from July 2012 to December2013 of Wangxingzhuang mine were analyzed by using GM（1 ,2） method, meanwhile, the aquifer level fall wasconsidered when the GM（1 ,2） model was established, and the fitting result was analyzed. The water inflow datafrom January to April 2014 were predicted and compared with the observed data, and GM（1 ,2） was comparedwith GM （1 ,1 ） on the forecast results. The results showed that the prediction accuracy of GM （1 ,2 ） was97.44% ,and GM（1 ,1） was 92. 60% . Prediction accuracy has been improved significantly using GM （1,2）.
出处
《河南理工大学学报（自然科学版）》
CAS
北大核心
2016年第3期368-372,共5页
Journal of Henan Polytechnic University（Natural Science）
基金
国家自然科学基金资助项目(41272250
41573095)
河南省高校科技创新团队支持计划项目(15IRTSTHN027)
河南省高校重点科研项目计划(16A170010)
