An Improved Potential Groundwater Yield Zonation Method for Sandstone Aquifers and Its Application in Ningxia, China

Li, Liangning; Li, Wenping; Shi, Shouqiao; Yang, Zhi; He, Jianghui; Chen, Weichi; Yang, Yuesuo; Zhu, Ting; Wang, Qiqing

doi:10.1007/s11053-022-10021-2

Cited by 7 publications

(3 citation statements)

References 36 publications

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“…Therefore, accurate and efficient prediction of the water inflow per unit of the roof aquifer can identify the principal areas for preventing and controll water damage to the roof in coal seams, propose timely, scientific and effective prevention and remedial measures to significantly reduce the incidence of roof water damage accidents in coal mines [4,5]. Numerous studies have investigated the prediction of aquifer water abundance, including the use of conventional methods [5][6][7][8][9][10][11][12][13], as well as combining these methods with GIS data management and spatial analysis to classify areas of varying water abundance [14][15][16][17][18][19]. The conventional means of predicting the aquifer's water abundance, along with other linear mathematical methods, are affected greatly by site data and human factors, and the calculation index weight is subjective and error is large, so it is difficult to accurately show the aquifer's water abundance.…”

Section: Introductionmentioning

confidence: 99%

A Prediction Model of Coal Seam Roof Water Abundance Based on PSO-GA-BP Neural Network

Dai,

Li,

Zhang

et al. 2023

Water

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With the gradual increase of coal production capacity, the issue of water hazards in coal seam roofs is increasing in prominence. Accurate and effective prediction of the water content of the roof aquifer, based on limited hydrogeological data, is critical to the identification of the central area of prevention and control of coal seam roof water damage and the reduction of the incidence of such accidents in coal mines. In this paper, we establish a prediction model for the water abundance of the roof slab aquifer, using a PSO-GA-BP neural network. Our model is based on five key factors: aquifer thickness, permeability coefficient, core recovery, number of sandstone and mudstone interbedded layers, and fold fluctuation. The model integrates the genetic algorithm (GA) into the particle swarm optimization (PSO) algorithm, with the particle swarm optimization algorithm serving as the primary approach. It utilizes adaptive inertia weight and quadratic optimization of the weights and thresholds of the backpropagation neural network to minimize the output error threshold for the purpose of minimizing output errors. The prediction model is applied to hydrogeology and coal mine production for the first time. The model is trained using 100 data samples collected by the Surfer 13 software. These samples help to accurately predict the unit inflow of water. The model is then compared with traditional forecasting methods such as FAHP, BP, and GA-BP neural network models to determine its efficiency. The study found that the PSO-GA-BP neural network model accurately predicts aquifer water abundance with higher precision. The root mean square error (RMSE) of the test set is determined to be 8.7 × 10−4, and the fitting result is measured at 0.9999, indicating minimal error with actual values of the sample. According to the prediction results of the test set, the water abundance capacity of the No. 7 coal mine in Hami Danan Lake is divided, and it is found that the overall difference between the results and the actual value is small, which verifies the reliability of the model. According to the results of the water abundance division, strong water abundance areas are mainly concentrated in the third-partition area. This study provides a new method for the prediction of aquifer water abundance, improves the prediction accuracy of aquifer water abundance, reduces the cost of coal mine production, and provides a scientific evaluation method and a theoretical basis for the prevention and control of water disasters in coal seam roofs.

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Section: Introductionmentioning

confidence: 99%

A Prediction Model of Coal Seam Roof Water Abundance Based on PSO-GA-BP Neural Network

Dai,

Li,

Zhang

et al. 2023

Water

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“…In terms of risk prediction of the water inrush in working faces in coal mines, the ‘three maps and two predictions method’ and the ‘water-richness index method’ are commonly used for the evaluation of the risk of roof water inrush. − For the evaluation of the risk of water inrush from the floor, a vulnerable index method is often used. − On this basis, Chinese scholars have proposed a large number of evaluation methods and established corresponding engineering geological models and hydrogeological models to assess the risk of water inrush before the drainge. ,,− However, in some cases, water inrush occurred after the drainage. To our best knowledge, few studies on the multifactor evaluation of the risk of water inrush from the roof after drainage have been conducted.…”

Section: Introductionmentioning

confidence: 99%

A Multifactor Quantitative Assessment Model for Safe Mining after Roof Drainage in the Liangshuijing Coal Mine

et al. 2022

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To prevent coal mine roof water damage, the water generally needs to be evacuated in advance. It can be mined with the water inrush risk assessed as safe. However, a single index is often employed in the water safety evaluation after the roof drainage, which causes a large gap between the evaluation results and the actual situation. Therefore, the evaluation cannot be effectively used to guide the safety mining in the working face. In this paper, based on the hydrogeological data of the Liangshuijing coal mine, a multifactor water inrush risk assessment model (IAHP-EWM) and multifactor index system are established for assessing the water inrush risk before and after the roof drainage. The improved AHP method and the entropy weight method are adopted in the model to determine the index weight. This combined way avoids the excessive subjectivity and objectivity of the index weight. A″ Fold undulation degree ( F ud )″ is innovatively proposed to quantify the impact of the spatial relief of folds on water inrush in the multifactor index system. The IAHP-EWM model is applied to evaluate the risk of roof water inrush in the 42205 working face of the Liangshuijing coal mine. The evaluation results show that the water inrush risk is ″high″ when the water is not dredged, and the water inrush risk is ″low″ after the water is dredged, which are consistent with the actual water inflow data and evaluation results, which verifies the accuracy of the model. The application results of the IAHP-EWM model in the 42202, 42203, and 42204 working faces verify its universal applicability in the Liangshuijing mining area. It can provide a reference for the evaluation of the roof water damage control effect during coal seam mining.

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“…Over the past few decades, geologists in different fields have tried different methods to study the effect of faults on fluid flow, including laboratory tests such as hydrochemical analysis and core testing and field tests such as pumping tests and borehole geophysical analysis. − These studies have shown that the hydraulic conductive property of faults may depend on field-scale geological features, groundwater conditions, fillings/granular rocks within the fault zone, rock mineralogy, and other influential aspects. These studies have enriched the theory of fault hydraulic conductive property.…”

Section: Introductionmentioning

confidence: 99%

Method for Evaluating Fault Hydraulic Conductive Property and Its Application in Shandong, China

et al. 2022

ACS Omega

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Coal remains the largest contributor to the energy structure of China. However, coal production is frequently threatened by groundwater inrush accidents caused by hydraulically conductive faults. Despite the threat of such accidents, research on methods for evaluating fault hydraulic conductive property without hydraulic tests has seldom been conducted. Many faults exist in coal mines in Shandong, China. However, due to economic and technical limitations, hydrological tests are rarely performed and can be performed on only a few faults. The hydraulic conductive property of many faults is unknown, which has prevented serious groundwater inrush accidents and casualties from being avoided. Using accessible geological exploration data, we propose a method for evaluating fault hydraulic conductive property in the Jining coalfield, Shandong, China. Mudstone smearing, lithologic contact relations on the fault plane, geostress, water pressure, plastic deformation of mudstone, and the argillaceous content of the fault zone were selected as factors, and six quantitative indicators were proposed: the shale gouge ratio (SGR), lithologic juxtaposition diagram (LJD), fault closure coefficient (FCC), water pressure coefficient (WPC), mudstone deformation coefficient (MDC), and shale smear factor (SSF). The fuzzy analytic hierarchy process (FAHP) was applied to calculate the weights and establish lateral and vertical hydraulic conductive property (L and V) evaluation models for faults. The fault hydraulic conductivities were then classified as weak, medium, or strong. The hydrochemical experiments and the limited number of exposed faults were used for validation. Hence, the evaluation models were considered effective at determining the hydraulic conductive property of faults in the Jining coalfield, China.

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An Improved Potential Groundwater Yield Zonation Method for Sandstone Aquifers and Its Application in Ningxia, China

Cited by 7 publications

References 36 publications

A Prediction Model of Coal Seam Roof Water Abundance Based on PSO-GA-BP Neural Network

A Prediction Model of Coal Seam Roof Water Abundance Based on PSO-GA-BP Neural Network

A Multifactor Quantitative Assessment Model for Safe Mining after Roof Drainage in the Liangshuijing Coal Mine

Method for Evaluating Fault Hydraulic Conductive Property and Its Application in Shandong, China

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