Modeling abrupt changes in mine water inflow trends: A CEEMDAN-based multi-model prediction approach

Abstract

The global impact of coal mining on groundwater resources is increasingly prominent. Constructing waterproof curtains can mitigate the destruction caused by open-pit coal mining on groundwater resources. However, the construction of these curtains alters the hydrogeological conditions, leading to abrupt changes in mine water inflow that are challenging to predict. Traditional models like Long Short-Term Memory Neural Network (LSTM) and Gated Recurrent Unit Neural Network (GRU) demonstrate poor performance when predicting abrupt changes in data trends. This study proposes a multi-model prediction approach and a Self-Attention-based prediction model to address this issue. The proposed Approach, based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), leverages the strengths of multiple models for prediction. The research results found that the proposed Self-Attention model excels at forecasting abrupt shifts in data trends. Comparatively, the multi-model predictions show a 26.12 % improvement in Mean Absolute Error (MAE) and a 33.47 % improvement in Root Mean Square Error (RMSE) when compared to single-model predictions. This study not only unveils the potential of Self-Attention in predicting abrupt trend changes but also integrates it with traditional LSTM and Support Vector Machine (SVM) models. The multi-model prediction framework proposed in this study integrates the strengths of multiple models, achieving higher accuracy than a single model. Accurate prediction of mine water inflow is essential, as it can provide valuable insights for decision-making by coal mining enterprises and local regulatory authorities, offering a scientific basis for effective management.