PDF(Pubmed)
Abstract：
Disasters caused by mine water inflows significantly threaten the safety of coal mining operations. Deep mining complicates the acquisition of hydrogeological parameters, the mechanics of water inrush, and the prediction of sudden changes in mine water inflow. Traditional models and singular machine learning approaches often fail to accurately forecast abrupt shifts in mine water inflows. This study introduces a novel coupled decomposition-optimization-deep learning model that integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Northern Goshawk Optimization (NGO), and Long Short-Term Memory (LSTM) networks. We evaluate three types of mine water inflow forecasting methods: a singular time series prediction model, a decomposition-prediction coupled model, and a decomposition-optimization-prediction coupled model, assessing their ability to capture sudden changes in data trends and their prediction accuracy. Results show that the singular prediction model is optimal with a sliding input step of 3 and a maximum of 400 epochs. Compared to the CEEMDAN-LSTM model, the CEEMDAN-NGO-LSTM model demonstrates superior performance in predicting local extreme shifts in mine water inflow volumes. Specifically, the CEEMDAN-NGO-LSTM model achieves scores of 96.578 in MAE, 1.471% in MAPE, 122.143 in RMSE, and 0.958 in NSE, representing average performance improvements of 44.950% and 19.400% over the LSTM model and CEEMDAN-LSTM model, respectively. Additionally, this model provides the most accurate predictions of mine water inflow volumes over the next five days. Therefore, the decomposition-optimization-prediction coupled model presents a novel technical solution for the safety monitoring of smart mines, offering significant theoretical and practical value for ensuring safe mining operations.
摘要：
矿井水流入造成的灾害极大地威胁着煤矿开采作业的安全。深部开采使水文地质参数的获取复杂化,涌水的机制，以及矿井涌水量突变的预测。传统模型和单一机器学习方法通常无法准确预测矿井涌水量的突然变化。本研究引入了一种新颖的耦合分解-优化-深度学习模型，该模型集成了完整的经验模态分解与自适应噪声（CEEMDAN），北方苍鹰优化(NGO)，和长短期记忆(LSTM)网络。我们评估了三种类型的矿井涌水量预测方法：奇异时间序列预测模型，分解-预测耦合模型，和分解-优化-预测耦合模型，评估他们捕捉数据趋势突然变化的能力及其预测准确性。结果表明，奇异预测模型是最优的，滑动输入步长为3，最大周期为400。与CEEMDAN-LSTM模型相比，CEEMDAN-NGO-LSTM模型在预测矿井涌水量的局部极端变化方面表现优异。具体来说,CEEMDAN-NGO-LSTM模型在MAE中获得96.578分，1.471%的MAPE，122.143inRMSE,和0.958的NSE，与LSTM模型和CEEMDAN-LSTM模型相比，平均性能提高了44.950%和19.400%，分别。此外,该模型提供了未来五天矿井涌水量的最准确预测。因此,分解-优化-预测耦合模型为智能矿山的安全监控提供了一种新颖的技术解决方案，为确保安全采矿作业提供了重要的理论和实践价值。
