在煤矿采掘过程中,因矿井涌(突)水造成的人员和财产损失极为严重。为预防涌(突)水灾害事故的发生,掌握涌水量的发展变化规律,开展涌水预测预报尤其是矿井涌水量的精准预计尤为重要,是矿井水害防治中一项重要的工作任务。为提高矿井涌水量的预测准确性,针对随时间无明显变化规律的涌水量序列,提出了变分模态分解(Variational Mode Decomposition,VMD)和深度置信网络(Deep Belief Network,DBN)相结合的高效时间序列预测模型。首先通过VMD模态分解技术对原始数据进行去噪,将原始矿井涌水量时间序列分解为若干个本征模态函数(Intrinsic Mode Function,IMF)分量,使各个IMF分量都具有原始时间序列在不同时间尺度下的统计学特征量,降低了原始时间序列的强震荡性和非稳定性。其次针对每个IMF分量,分别建立各自的DBN模型进行训练学习,进而建立起相应的预测网络模型。最后融合各分量预测值得到最终结果。结果显示,VMD-DBN的EMA, EMAP, ERMS and R2 of VMD-DBN are 9.23, 0.76%, 11.55 and 0.97 respectively, which are compared with the predicted values of GA-BP, LSTM, VMD-LSTM, RBM, VMD-RBM, and DBN models, finding that the mine water inrush prediction with VMD-DBN model has a higher accuracy. Therefore, the VMD-DBN model has relatively obvious advantages under the conditions that the water inrush has no obvious change law over time but with strong oscillation and instability, thus enriching the mine water inrush prediction methods, providing a new technical means for the intelligent mine safety monitoring, with some theoretical value and practical significance. 相似文献