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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. • A novel multi-model prediction framework is introduced. • An prediction model based on Self-Attention is proposed. • Uncovering Self-Attention's untapped trend forecasting potential. [ABSTRACT FROM AUTHOR]

Due to the extremely complicated hydrogeological environment, significant symptoms of water inrush can not be detected accurately using normal exploratory methods, which produces hundreds of water inrushes occurred during tunnel construction in karst area. This study aims to present a new water inflow prediction technique without considering the relationship between hydrogeological features and water discharge rate. Therefore, the nonlinear regression Gaussian process analysis is applied to develop a model for predicting water inflow into tunnels. In order to meet the requirement of the data format of Gaussian process regression model (GPR), the basic evaluation index system of water inflow into tunnels and corresponding criterion are set up and quantified based on the statistical information of water inrush cases. To verify its feasibility, The GPR model is applied to Zhongjiashan tunnel on Jilian highway in China. The results of the comparisons indicate that the prediction results obtained from the GPR model are generally in a good agreement with the field-observed results. The proposed Gaussian process, on the whole, performs better than the support vector machine (SVM) and artificial neural network (ANN) in predictive analysis of water inflow into tunnels. [ABSTRACT FROM AUTHOR]

[Display omitted] • The random deterministic three-dimensional fracture coupling model is constructed. • A new fracture search algorithm: full space parallel search algorithm is proposed. • Based on this, the accurate prediction of water inflow of the tunnel is realized. With the rapid development of national infrastructure construction, the water inrush disaster of "strong burst, high water pressure and large flow" is more and more frequent in the construction process. To achieve high-precision prediction of tunnel water inflow, based on the Yue Longmen Tunnel, according to the structural plane detection data in the study area, combined with the Monte Carlo algorithm and using "parent-daughter" and "step–structure" correction mode, this paper constructs a random deterministic three-dimensional fracture network seepage model. Based on the three-dimensional fracture network seepage model and the principle of flow balance, the water inflow of each mileage section of the Yue Longmen Tunnel is solved by numerical calculation method, and good results are obtained through comparison and verification. The research results have important theoretical significance and engineering application value for tunnel water inflow prediction. [ABSTRACT FROM AUTHOR]

• Permeability coefficients are obtained based on water inflow in probe drilling holes. • Formulas of the water inflow are derived based on the superposition principle. • Factors affecting the water inflow in subsea tunnels are analyzed. • Prediction formulas of water inflow are verified by field test and numerical simulation. Prediction of water inflow is a key scientific problem in construction of subsea tunnels. Tsingtao subsea tunnel is taken as the research object, and a calculation method for predicting and analyzing the water inflow based on superposition principle is proposed by applying theoretical analysis, numerical simulation and field monitoring. First of all, the computation model of seepage field and water inflow in the subsea tunnel is established. The permeability coefficients of strongly weathered layer and moderately weathered layer are determined according to the permeability coefficient of slightly weathered layer. Secondly, based on the well flow model of infinite confined aquifer, the double water gushing points calculation model is presented, and then the model is simplified into single water gushing point calculation model. The equivalent calculation formulas of water inflow under the condition of multi-horizontal strata are derived. The water inflow which is affected by the tunnel depth, radius and seawater depth is analyzed. Thirdly, the proposed method is compared with the engineering practice and numerical simulation, which verifies the accuracy and feasibility of the method. The results show that: (1) The water inflow of subsea tunnels increases with the permeability coefficients of strongly and moderately weathered layers. (2) The water inflow of subsea tunnels first decreases and then increases with the tunnel depth increasing. (3) The water inflow of subsea tunnels increases linearly with the tunnel radius and seawater depth. [ABSTRACT FROM AUTHOR]