[关键词]
[摘要]
针对马尔科夫链预测的局限性, 提出了能够进行清晰定量计算的叠加矿井涌水量的马尔科夫链预测方法。基 于 2008 年 1月- 2013 年 12 月成庄煤矿 72个月的涌水量资料, 进行涌水量状态分级, 计算状态转移矩阵, 将不同步 长转移矩阵求得的预测值进行叠加平均, 进而建立了叠加马尔科夫链预测模型, 分析拟合效果, 预测了 2014 年 1 月- 4 月的涌水量, 并与实测值进行了对比。结果表明, 该模型的预测精度达到了 94184%, 预测效果较好, 从而为矿井 涌水量的预测提供了一种新方法。
[Key word]
[Abstract]
Superimposed Markov chain was proposed to predict mine water inflow quantitatively since the general Markov chain has limitations. . Based on the water inflow data in the Chengzhuang coal mine from January 2008 to December 2013( 72 months) , water inflow status was classified, state transition matrix was calculated, the predicted values from different step ma 2 trixes were superimposed and averaged, and thus the superimposed Markov chain model was built and the fitting results were analyzed. The water inflow data from January to April 2014 were predicted and compared with the observed data. The results showed that the model prediction accuracy is about 94. 84%, so this new method can be used for mine water inflow prediction.
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[基金项目]
国家自然科学基金/ 基于水化学关键因子的相似矿区煤层底板突水水源的识别0( 41272250)