Prediction Of Water Inflow Research Articles

Prediction technology of mine water inflow based on entropy weight method and multiple nonlinear regression theory and its application

Mine water inflow is an important basis for the formulation of mining plans and the utilization of groundwater resources. The mine water inflow is the result of the combined influence of many factors. The weight value of the influencing factors is calculated by the entropy method, and the order of importance of the factors is: precipitation > mining depth > cumulative mined-out area > aquifer thickness > mining area > mining height. The optimal univariate nonlinear regression model of mine water inflow to each influencing factor is obtained by factor scatter analysis and Matlab function programming. On this basis, combined with the weight values of factors, a multivariate nonlinear regression prediction model of mine water inflow based on weighting is innovatively established, which overcomes the defect that the traditional water inflow prediction method that cannot reflect the relative importance differences of various influencing factors. The multivariate weighted nonlinear regression model is used to predict the mine water inflow of typical coal mines, and the prediction results are compared with the linear regression model and the measured value. The results show that the prediction model of mine water inflow based on weighted multivariate nonlinear regression is accurate higher, with higher practical application value.

Predicting Water Inflow in Tunnel Construction: A Fracture Network Model with Non-Darcy Flow Considerations

Fractures are widely distributed in karst areas, and when flow rates are high, they exhibit complex nonlinear behavior that cannot be accurately described by Darcy’s law. In this work, a hydro-mechanical coupling model based on a discrete fracture network is proposed to predict tunnel water inflow, accounting for the impact of non-Darcy flow. The model’s feasibility has been validated by comparing it with experimental results and the field measurements of flow rates at the Bodaoling Tunnel in Guizhou, China. The results show that Darcy flow tends to overestimate water inflow by approximately 25% compared to non-Darcy flow. The non-Darcy effect grows with the increase in initial fracture width and empirical constant q. When q exceeds 8.77 × 10−6, the growth rate of the Forchheimer number along the fracture width slowed down, and the inhibitory effect of non-Darcy flow on flow became gentle. Additionally, in a complex fracture network, the inflow rate limited by non-Darcy flow at one point drives the water flow through a connect fracture to another point, which increases the difficulty in water inflow prediction. This work highlights the importance of non-Darcy flow and fracture networks when accurately predicting water inflow in tunnels.

Investigation of the Tunnel Water Inflow Prediction Method Based on the MODFLOW-DRAIN Module

The precise prediction of water inflow in tunnels is a key aspect of civil engineering. It is directly related to the progress of construction, the stability of caverns and the safety of construction personnel. Currently widely used calculation methods include the empirical formula method, the analytical method and the numerical simulation method. In situations where the geological conditions are complex and the boundary conditions are irregular, numerical simulation methods have clear advantages. However, there are still discussions about the rationality, accuracy and applicability of the definition of tunnel boundary conditions in numerical simulation methods. Based on the Groundwater Model System (GMS10.7) software, we investigate the feasibility of using the DRAIN module to describe tunnel boundaries and calculate water inflow. By comparing it with traditional empirical formulas, the feasibility of the method is verified, and the error is about 8%. In addition, this method is applied to predict the water inflow in the construction of an underground pumping station under complex geological conditions and the prediction is compared with the measured water inflow, and the error is about 20%. This article explains the physical meaning and the value method of the relevant parameters of the DRAIN module, providing basic support for the prediction of water inflow in tunnels.

Prediction of water inflow and analysis of surrounding rock stability in unfavorable geological mountain tunnel

During the construction of a mountain tunnel, water inflow and rock instability are common occurrences due to unfavorable geological conditions, posing serious threats to construction safety. This study focuses on a proposed mountain tunnel and employs multiple formulas to predict potential water inflow during excavation. Based on the amount of water inflow and deformation of surrounding rocks, comprehensive determinations are made for the thickness of grouting rings and permeability coefficients. The results demonstrate that: 1) Different formulas yield slightly varied outcomes but overall trends remain consistent; considering various calculations, the normal water inflow for this tunnel is approximately 115.5908×103 m3/d with a maximum at 210.9100×103 m3/d 2) Increasing grouting ring thickness or decreasing permeability coefficient can effectively reduce water inflow, but the reduction range is gradually narrowed. 3) Pre-grouting curtains have an evident effect in enhancing stability; however, their effectiveness decreases with increased thickness. 4) Taking into account both safety and economic factors, it is recommended that the grouting ring thickness be set at 8 m with a permeability coefficient equaling one 100th that of surrounding rocks for this tunnel project.

A dynamic modeling approach to predict water inflow during tunnel excavation in relatively uniform rock masses

Karst landforms are widely distributed all over the world. With the continuous improvement of infrastructure construction, tunnelling in mountainous karst areas is inevitable. Due to the complex hydrogeological conditions in karst areas, groundwater inflow during tunnelling is usually difficult to predict and can cause huge economic losses and casualties. Therefore, finding better ways to improve the prediction of tunnel water inflow is of great significance to ensure the safety of tunnelling. In this study, three commonly used modules (CHD, DRAIN and CFPM1) in MODFLOW are selected and compared to predict the water inflow of tunnels. We find that the CFPM1 module performs better in terms of considering both tunnel size and actual water inflow dynamics during tunnel excavation, which is a continuous process rather than an instantaneous process. Secondly, we build a hypothetical case and predict the tunnel water inflows using above three different modules. We find that the CHD module yields the largest initial and stable water inflow, followed by the DRAIN module, and the CFPM1 yields the least. Besides, as the most sensitive factor in the CFPM1 module, the influence of tunnel diameter on water inflow is discussed. Finally, taking the Shizishan Tunnel as an actual field example, we compare the simulation results of different modules with the measured tunnel inflow. In general, the prediction of the CFPM1 module is the closest to the measured value, and the prediction accuracy is the highest during the conventional tunnel construction process. However, when water-saturated karst features such as underground rivers, caves or faults are exposed during tunnelling, the water level may change instantaneously, which is more consistent with the principle of CHD module. In summary, this study provides theoretical and numerical support for the safe construction of relatively uniform rock mass tunnels in karst areas.

Water inflow prediction and waterproof-drainage system optimization in undersea tunnel

This study focuses on the Shantou Bay Undersea Tunnel and engages in theoretical research to develop a water inflow prediction formula associated with pore water pressure, building upon Darcy’s law. The formula has stronger adaptability. Moreover, Grey Relational Analysis is conducted based on theoretical formulas considering various factors to identify the factors influencing the circumferential water inflow ranking in the tunnel. Subsequently, stability analysis of undersea tunnel waterproof and drainage systems is carried out, considering three forms of waterproof and drainage: full-encapsulation non-discharge, full-encapsulation controlled-discharge, and semi-encapsulation free-discharge. The results indicate that, in cases involving grouting, the water inflow prediction formula correlates with seven factors, including the grouting ring permeability coefficient and tunnel dimensions. Grey Relational Analysis reveals that the three most influential factors affecting water inflow in the undersea tunnel are, respectively, the far-field hydraulic head, tunnel outer diameter, and grouting ring permeability coefficient. Analyzing the seepage field distribution around the tunnel bore, it is observed that grouting has the most significant improvement in the seepage field for the full-encapsulation controlled-discharge form. Regarding water seepage, grouting has the most substantial impact on improving water seepage for the semi-encapsulation free-discharge form. Considering the displacement field, reductions are more pronounced in both the full-encapsulation controlled-discharge and semi-encapsulation free-discharge forms. Therefore, in conjunction with radial grouting, adopting a full-encapsulation controlled-discharge drainage system is more suitable for practical engineering applications. This approach reduces both economic costs and the risk of water inflow in the undersea tunnel.

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

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.

Hybrid Approach for Early Warning of Mine Water: Energy Density-Based Identification of Water-Conducting Channels Combined With Water Inflow Prediction by SA-LSTM

Hybrid Approach for Early Warning of Mine Water: Energy Density-Based Identification of Water-Conducting Channels Combined With Water Inflow Prediction by SA-LSTM

Analysis of the Impact of Hydrological Parameters on Mine Water Inflow Prediction in Environmental Impact Assessment

Analysis of the Impact of Hydrological Parameters on Mine Water Inflow Prediction in Environmental Impact Assessment

METHOD OF PREDICTING SEASONAL VARIATIONS IN WATER INFLOW TO UNDERGROUND MINE WORKINGS USING THE WATER CATCHMENT MODEL WITH CONCENTRATED PARAMETERS

The authors suggest using the water catchment model with concentrated parameters for prediction of seasonal variation in water inflow to mines. The efficiency of the slope runoff model application was previously shown on the base of three test subjects operating apatite-nepheline ore deposits in the Khibiny massif. The article provides a simple methodology for water inflow prediction which doesn’t require any specific software. The example is provided of using this methodology for monthly water inflow prediction based on an underground mine in Kuzbass.

Response Characteristics and Water Inflow Prediction of Complex Groundwater Systems under High-Intensity Coal Seam Mining Conditions

With the gradual improvement in coal mining efficiency, the disturbance of groundwater systems caused by high-intensity mining also increases, leading to challenges in maintaining mine safety and protecting water resources in mining areas. How to accurately describe the dynamic changes in the groundwater system under mining and quantitatively predict mine water inflow are currently major problems to be addressed. Based on a full analysis of the response characteristics of a groundwater system to the extraction disturbance, this paper presents a new method to establish a mine hydrogeological conceptual model that can accurately represent the water inrush process. The unstructured-grid package of MODFLOW is used to accurately characterize the formation structure and finally make accurate water inflow predictions. Taking the Caojiatan coal mine in Shaanxi Province, China, as an example, a numerical model of unstructured water inflow is established, and the changes in the water inflow source and intensity are quantitatively evaluated. Compared with the traditional water inflow prediction method, the prediction accuracy of the new model is improved by 12–17%, which is achieved by detailing the response of the complex groundwater system under high-intensity mining conditions. The method presented in this paper has great significance and applicatory value for obtaining a comprehensive understanding of the disturbance characteristics of human underground engineering activities (e.g., coal mining) on groundwater systems, as well as accurately predicting water inflow.

Optimal Determination and Dynamic Control Analysis of the Graded and Staged Drought Limit Water Level of Typical Plateau Lakes

The technical research on determining the drought limit water level can be used as an important basis for starting the emergency response of drought resistance in the basin and guiding the drought resistance scheduling of water conservancy projects. When the concept of drought limit water level was first proposed, the main research object was reservoirs, and the method for determining the lake drought limit water level was not established. Referring to the calculation method of reservoir drought limit water level, the drought limit water level is used as a single warning indicator throughout the year, which lacks graded and staged standards, and also lacks rationality and effectiveness in practical application. Therefore, this article has improved the concept of lake drought limit water level (flow). Under different degrees of drought and water use patterns during the drought period, combined with the characteristics of lake water inflow, considering the factors such as ecology, water supply, and demand, lake inflow, evapotranspiration loss, a graded and staged standard of lake drought limit water level has been developed. For different types of lakes, a general method for determining the lake’s graded and staged drought limit water level has been established. The SCSSA-Elman neural network is used to construct the medium and long-term water inflow prediction model for lakes, and the calculation results of this model are used for the warning and dynamic control analysis of the lake drought limit water level. The application of this method has the characteristics of strong applicability and high reliability. Finally, the determination method and dynamic control method of the lake’s graded and staged drought limit water level have been successfully applied at Dianchi Lake in Yunnan.

Enhancing the performance of tunnel water inflow prediction using Random Forest optimized by Grey Wolf Optimizer

Enhancing the performance of tunnel water inflow prediction using Random Forest optimized by Grey Wolf Optimizer

Semi-empirical models for predicting stable water inflow and influence radius of a tunnel considering non-Darcian effect

How to accurately predict water inflow and relevant groundwater influence of a tunnel are classical scientific issues in the field of hydrogeology, which are also of great significance to the construction and operation of underground engineering. This issue is usually investigated based on the assumption of the linear Darcy's law. However, the tunnel inflow is usually featured by high velocities or high pressures, especially in karst regions, which allows the flow regime to easily develop into non-Darcian flow regime. The impact of the non-Darcian effect on predicting water inflow and influence radius of a tunnel has not yet been systematically examined. Here, we numerically investigated the prediction of stable water inflow and influence radius under non-Darcian flow regime. By numerically solving the continuity equation of seepage flow incorporating the nonlinear Forchheimer law, a series of numerical simulations were conducted for stable water inflow into a tunnel by varying key controlling parameters. The quantitative dependencies of stable water inflow on these parameters were figured out, based on which a semi-empirical stable water inflow model was developed considering the non-Darcian effect. Meanwhile, the magnitude of the influence radius was investigated by statistical analysis of 59 actual engineering cases and estimated using existing empirical formulas. Based on these preliminary explorations, a semi-empirical model for estimating influence radius was concluded from massive simulations. The validities of our proposed semi-empirical models for stable water inflow and influence radius were further numerically verified. Comparative study and application in a real engineering case showed that compared with our proposed models, most existing empirical methods predicted a larger stable water inflow and a smaller influence range of groundwater. Besides, comparative results of proposed models with omitting non-Darcian term indicated that neglecting non-Darcian effect would overestimate both the stable water inflow and influence radius. These findings and established models would be useful for assessing stable water inflow and influence radius of a tunnel under non-Darcian flow regime.

Construction and application of mine water inflow prediction model based on multi-factor weighted regression: Wulunshan Coal Mine case

Construction and application of mine water inflow prediction model based on multi-factor weighted regression: Wulunshan Coal Mine case

Contribution of temperature measurements to the hydrogeological model in the Snowy 2.0 deep tunnels

This paper refers to experiments developed on the deep tunnels design of the Snowy 2.0 pumped-storage hydroelectric project in NSW, Australia. The 27 km long waterway and the power station complex are about 800 m below ground, and are in a complex tectonic and lithological setting. Long-term temperature monitoring in boreholes has been used to infer an empirical thermal model along the deep tunnels, which is presented as an example of input data for the ventilation design during construction, and for durability design during operation. An innovative aspect of this paper is related to the graphical interpretative method of borehole temperature logs that is proposed to infer downward or upward groundwater flows and predict the hydrogeological behaviour of fractured and faulted zones that cross the tunnel alignment. A large campaign of thermal acquisition in boreholes, using an acoustic televiewer probe, identified local anomalies of the geothermal gradient, which were correlated to permeability and pressure data measured in the boreholes, to identify zones of greater permeability that intersect the tunnels. Furthermore, the application of thermal measurements during tunnel excavation is proposed as an additional tool for the prediction of water inflow or identification of zones to be pre-grouted and/or drained. Thematic collection: This article is part of the Leading to Innovative Engineering Geology Practices collection available at: https://www.lyellcollection.org/topic/collections/leading-to-innovative-engineering-geology-practices

Prediction of tunnel water inflow based on stochastic deterministic three-dimensional fracture network

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.

Application of robust deep learning models to predict mine water inflow: Implication for groundwater environment management

Traditional mine water inflow prediction is characterized by a high degree of uncertainty in model parameters and complex mechanisms involved in the water inflow process. Data-driven models play a key role in predicting inflow mechanisms without considering physical changes. However, the existing models are limited by nonlinearity and non-stationarity. Thus, the principal objective of this study was to propose two robust models, the DIFF-TCN model and the DIFF-LSTM model, for predicting the average water inflow per day. The models consist of three methods, namely Difference Method (DIFF), Temporal Convolutional Neural Network (TCN), and Long Short-Term Memory Neural Network (LSTM). When applied to the Tingnan Coal Mine, Shanxi Province, China, the DIFF-TCN performs better in predicting the average daily water inflow, the model has a MAE of 5.88 m3/h, RMSE of 6.85 m3/h and R2 of 0.96 in the test stage of the water inflow event. Comparison with the other deep learning models (with similar complex structures) and traditional time series model shows the superiority of our proposed DIFF-TCN model. The SHAP value is used to explain the contribution of each model input to the predicted values, and it indicates that the historical time of water inflow data are the most important input, and the advance distance and the groundwater level data also contribute to the model predictions, but groundwater level data for some periods in the past may have a detrimental effect on the model. The findings of this study can provide better understanding about potential of robust deep learning models for smart hydrological forecasting, and they can also provide technical guidance for mining safety production and protection of water resources and water environment around the mining area.

贵州老鹰岩隧道水文地质特征及涌水量预测

贵州老鹰岩隧道水文地质特征及涌水量预测

Determination of The Best Box-Jenkins’ Seasonal Model to Inflows of Reservoir of Bekhme Dam

Prediction of water inflow to a reservoir is of great interest in the policy of the reservoir operation throughout the year. When significant amounts of inflow series entering to the reservoir are nondeterministic events, the utilization of stochastic models to check the reliability of the recorded data and forecast the future events become preferable. One of the most powerful and widely used methodology for forecasting time series is the class of models called the Box-Jenkins models. In this study, time series analysis was applied to records of 69 monthly mean inflows to Bekhme reservoir, in the northern part of Iraq, for the water year period from 1933 to 2006. Nine multiplicative seasonal models were fitted to this series; these were the seasonal autoregressive integrated (SARI) (1, 1, 0) × (1, 1, 0)12, (2, 1, 0) × (1, 1, 0)12, and (1, 1, 0) × (2, 1, 0)12 models, the seasonal autoregressive integrated moving average (SARIMA) (1, 1, 1) × (1, 1, 1)12, (2, 1, 2) × (1, 1, 1)12, and (1, 1, 1) × (2, 1, 2)12 models, and the seasonal integrated moving average (SIMA) (0, 1, 1) × (0, 1, 1)12, (0, 1, 2) × (0, 1, 1)12, and (0, 1, 1) × (0, 1, 2)12 models. The unconditional sum of squares method was used to estimate the parameters of the models and to compute the sum of squared errors for each one. It was found that the best model which corresponded to the minimum sum of squared errors was the SIMA (0, 1, 1) × (0, 1, 1)12 model. The estimated moving average parameters of this model were 0.378 and 0.953 for both θ and Θ respectively. The adequacy of this model was checked by plotting the normalized cumulative periodogram which does not indicate nonrandomness of the residuals. Forecasts of monthly inflow for the period from October, 2002, to September, 2006 were graphically compared with observed inflow for the same period and since agreement was very precise, adequacy of the selected model was confirmed.