基于CEEMDAN-BO-BiGRU的矿井涌水量预测研究

Mine Water Inflow Prediction Based on CEEMDAN-BO-BiGRU

为提高矿井涌水量预测的准确度,基于涌水量数据的不稳定性及随机性,提出一种自适应噪声完备集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)、贝叶斯优化(Bayesian optimization,BO)与双向门控循环单元(bi-directional gated recurrent unit,BiGRU)相结合的矿井涌水量预测模型CEEMDAN-BO-BiGRU。所提模型通过CEEMDAN将涌水量数据分解为多个较平稳的固有模态分量(intrinsic mode function,IMF)和残差分量(residual components,Res),过滤数据噪声,提取数据不同时间尺度波动特征,降低预测误差。利用贝叶斯优化对BiGRU模型多个超参数进行迭代寻优,进一步提高模型的预测精度。之后对各分量进行超前1~3步预测,最终将各分量预测结果加和得到涌水量多步预测结果。以小庄煤矿矿井涌水量数据进行试验,并将CEEMDAN-BO-BiGRU预测结果与其他多种预测模型结果进行对比分析。结果表明:采用CEEMDAN-BO-BiGRU组合网络模型对矿井涌水量预测结果更准确,该方法对涌水量的短时预测提供了一种新思路。

In order to improve the accuracy of the mine water surge prediction,based on the instability and randomness of the water surge data,a mine water surge prediction model CEEMDAN-BO-BiGRU was proposed,combining complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),Bayesian optimization(BO)and bi-directional gated circulation unit(BiGRU).The proposed model decomposed the influx of water data into a number of smoother intrinsic mode function(IMF)and residual components(Res)through the CEEMDAN,filtered data noise,extracted of data fluctuations in different time scales characteristics,reduced the prediction error.Bayesian optimization was used to iteratively seek the optimization of multiple hyperparameters of the BiGRU model to further improve the prediction accuracy of the model.Afterwards,each component was predicted 1 to 3 steps ahead of the prediction,and finally the prediction results of each component were summed to obtain the multi-step prediction results of the water influx.The CEEMDAN-BO-BiGRU prediction results were compared and analyzed with other prediction models.The results show that the CEEMDAN-BO-BiGRU combined network model is more accurate for the prediction of mine water surges,and the method provides a new idea for the short-time prediction of water surges.