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 begin{document}${E_{{rm{MA}}}}$end{document}、 begin{document}${E_{{rm{MAP}}}}$end{document}、 begin{document}${E_{{rm{RMS}}}}$end{document}和 begin{document}$mathop Rnolimits^2 $end{document}分别为9.23、0.76%、11.55和0.97，通过与GA-BP、LSTM、VMD-LSTM、RBM、VMD-RBM和DBN模型的预测值进行对比发现，VMD-DBN模型进行矿井涌水量预测具有更高的预测精度。VMD-DBN模型对于涌水量随时间无明显变化规律、且具有较强震荡性和非平稳的工况具有相对明显的优势，丰富了矿井涌水量预测方法，为智慧矿山的安全监测提供一种新型的技术手段，具有一定的理论价值和现实意义。

In the process of coal mining, the loss of people and property caused by mine water inrush is extremely serious. To prevent the occurrence of water inrush accidents and grasp the law of change of water inrush, the water inrush prediction and forecasting, especially the accurate estimation of mine water inrush, is very important, which is also an important task in the prevention and control of mine water damage. To increase the prediction accuracy of mine water inrush, an efficient time series prediction model combining Variational Mode Decomposition (VMD) and Deep Belief Network (DBN) was proposed for the series of water inrush with no obvious change with time. Firstly, the original data were initially denoised by VMD to break up the original mine water inrush time series into multiple Intrinsic Mode Function (IMF) components, so that each IMF component has the statistical characteristic quantity of the original time series at different time scales, which reduces the strong oscillation and instability of the original time series. Secondly, DBN model was established separately to each IMF component for training and learning, and then the corresponding prediction network model was built. Finally, the predicted values of each component were fused as a result. The results show that the E_MA, E_MAP, E_RMS 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.