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

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 (Fud )″ 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.

Introduction

The Jurassic coalfield in northern Shaanxi, China is one of the 1.4-billion-ton coal bases in China. It has the advantages of high coal resource enrichment, best coal quality, and good development prospects.[1,2] With the increase of the development scale and mining intensity of the Jurassic coalfield, the water inrush accidents from the working face roofs become more and more serious.[3] For a long time, people have had insufficient understanding of the severity of roof water damage during the development of the Jurassic coalfield. In addition, the focus of the previous mine-water-control research has been on North China coalfields threatened by floor ash water, resulting in a weak research foundation for roof water hazards in the Jurassic coalfield. The reason for the formation of the water damage from the roof is still unclear, leading to a lack of effective prediction, prevention technology, etc. Therefore, it is of great practical significance to study the risk of water inrush in the roof aquifer and make an accurate evaluation and then propose effective water prevention measures for guiding the safe production of the coal mine.[3−5]

Many types of roof water disasters are recognized in Jurassic coalfields, such as separated strata water disasters, water inrush, and sand inrush, roof thick sandstone water disasters, burning rock water disasters, etc. The mechanisms for roof water inrush are different case to case.[3,6−8] One main mechanism for the water inrush is the roof failure after coal seam mining, which is thoroughly explored by scholars.[9−14] The major research method is conducting a similar model test. However, the experimental conditions are way different from the actual conditions in the mine, which makes the results less convincing.[15−17] What is more, the geological conditions, hydrogeological conditions, lithology, and mining conditions are different from one mining area to another mining area, the research results cannot be widely applied, and the existing conclusions are not suitable for the Liangshuijing coal mine. Therefore, the formation of the water damage from the roof is still unclear, leading to a lack of effective prediction and prevention technology.

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.[18−25] For the evaluation of the risk of water inrush from the floor, a vulnerable index method is often used.[26−31] 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.[5,23,32−50] 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. The research in this paper aims to fill this research gap.

The evaluation of water inrush risk after roof drainage is mainly based on a single factor (drilling site flow attenuation curve) during practical production. The evaluation results are inconsistent with the actual situations of the mining areas. The following problems exist: (1) There is a lack of clear quantitative evaluation thresholds, resulting in serious water inrush accidents even with an evaluation result marked as ‘low’. For example, in September 2020, a single index evaluation system was used in the 1012001 working face of the Yuanzigou coal mine. Serious roof inrush occurred during mining even with the evaluation result of the risk of roof water inrush as ″safe″. The maximum water inflow amounted to 570 m3/h, submerging the working face of the well and roadway. In October 2020, a roof water inrush accident occurred during mining after the 1309 working face of the Guojiahe coal mine used a single index to evaluate the risk of roof water inrush as ″safe″. Three water inrush phenomena occurred in this working face. The maximum instantaneous water inrush volumes are 200, 1200, are 500 m3/h, resulting in the shutdown of the working face. (2) There is no clear definition of the state of the flow attenuation curve of the drilling site where the working face can be safely recovered, and it mainly depends on the subjective experience of water prevention and control staffs. (3) The roof water inrush is the result of the combined results of a variety of complex factors such as water inrush sources, water inrush channels, mining, etc. A single index cannot reflect the combined results of multiple factors. Therefore, the multifactor evaluation model for assessing the risk of water inrush at the working face after roof water evacuation is established, which has important guiding significance for guiding the production of the mining areas.

Based on the geological and hydrogeological conditions of the Liangshuijing coal mine, a roof water inrush risk assessment index system before and after drainage is proposed and established in this paper. A “Fold undulation degree (F)” index is included in the system to quantify the impact of the fold spatial relief on water inrush. Based on GIS, improved analytic hierarchy process theory, entropy weight method, and fuzzy mathematics method, a mathematical model of roof water inrush risk assessment is established. The 42202, 42203, and 42204 working faces of the Liangshuijing coal mine are employed to verify the practicability and generality of the model.

Study Area

The Liangshuijing coal mine is located about 16 km west of Shenmu County, Shaanxi Province, with a mining area of 68.9 km2. The mine has an approved production capacity of 8 million tons/a and service life of 46.8 years. It is mined with longwall fully mechanized mining. The mining area is in the northwest inland with a temperate semi-arid continental climate. The average annual precipitation is 435.7 mm, the average annual evaporation is 1774.1 mm, and the coal seam mining elevation is 1120–1080 m. Coal 4–2 is the main mining seam in the mining area. The coal seam is shallowly buried with a burial depth of 13.45–160.92 m. It is located at the top of the second section of the Jurassic Yan’an Formation. The geographic location of the mining area is shown in Figure .

Figure 1

Liangshuijing coal mine traffic location map.

The strata of the mining area from old to new are Upper Triassic Yongping Formation (T3y), Middle Jurassic Yan’an Formation (J2y), Zhiluo Formation (J2z), Neogene Pliocene Baode Formation (N2b), Quaternary Middle Pleistocene Lishi Formation (Q2l), Upper Pleistocene Salawusu Formation (Q3s), and Holocene Aeolian Sand (Q4eol). The mine geological structure is simple, the stratum is gentle, and the dip angle is less than 1°. Only the wavy undulating anticline structure with an extremely wide amplitude is developed, and there is no large fold, fault, and magmatic activity. The bedrock of the coal seam 4–2 roof is thin, and the weathered bedrock develops on the top of the bedrock. The aquifer in the study area is the weathered bedrock aquifer of Yan’an Formation (J2y) and the Quaternary phreatic aquifer (Q4eol). The weathered bedrock aquifer is highly water-rich and is the main aquifer in the mining area. The weathered bedrock fissure water is the main factor affecting the safe production of the mine.

This paper uses the 42205 working face as an example to establish a roof water inrush risk assessment model before and after drainage. In the 42205 working face, 66 water exploration and drainage holes were constructed to drain the water in the aquifer. All the holes penetrated the weathered bedrock fissure aquifer, and the final hole was within 1 m of the laterite layer. The lithological columnar shape of the working face and the borehole profile are shown in Figure .

Figure 2

Lithology and drainage borehole profile.

Methods

Coal seam roof water inrush is affected by a variety of complex factors. These factors have the characteristics of uncertainty, randomness, and ambiguity, which are difficult to be evaluated with classical mathematical models.[51,52] Therefore, based on GIS, this paper constructs the membership degree of each assessment factor using fuzzy mathematical theory, determines the weight of influencing factors by coupling the improved analytic hierarchy process and the entropy weight method, and establishes a GIS-based multifactor coal seam roof water inrush risk assessment model (IAHP-EWM). The steps are as follows:[53]

Step 1: Determine the set of assessment factors and comments.

The assessment factor set is a set composed of various factors that affect the assessment object. It is U = {u1, u2, ..., un}. U is the assessment factor set; u1, u2, ..., un are the various factors.

The comment set is a set composed of possible results from assessment objects and assessment indicators. It is V = {v1, v2, v3, ..., vn}. V is the comment set; v1, v2, v3, ..., vn are the assessment levels of assessment factors and are generally divided into 3–5 levels.

Step 2: GIS-based single factor fuzzy assessment and construction of assessment matrix.

First, a single-factor assessment for the single-factor U (i = 1, 2, 3, ..., n) in the assessment factor set was made. The membership degree of the factor U to the assessment level V is R, and the single factor assessment set of the ith factor U can be obtained as R (R, R, ..., R). In this paper, the ArcGIS system is used to determine the membership degree of the assessment factors: Kriging space interpolation, grid calculator, and other tools are used to quantify the assessment factors to generate a dimensionless thematic map, and then the membership degree is determined by classifying the area element. The specific details are as follows:

Dimensionless of the thematic map.

To eliminate the conflict between different dimensions of the assessment indicators, eqs and 2 are used to process the indicators with dimensionless. If the indicator has a positive correlation with the object to be evaluated, it is a benefit-type indicator, and eq is used for dimensionless processing; otherwise, if the indicator has a negative correlation with the object to be evaluated, it is a cost-type indicator, and eq is used for processing.[54]

In the above equations, a is the attribute value of the assessment factor, and r is the data after dimensionless processing.

Determine the membership degree of assessment factors.

The assessment factor map is partitioned by the natural discontinuous point method in ArcGIS, and the partitioning principle is shown in eq .[55]

In eq , SSD is the sum of squares of total deviation; i,j is the classification serial number; A[K] is the value set of a classification; K = i, ..., j; mean is the average value of classification. The membership degree of the assessment factors is determined by the method of classifying the area element.[51]

In the above, r(x) is the membership degree of v, and Sij is the area of the jth classification area of the jth assessment index thematic map. Finally, the membership degrees of each assessment factor are arranged in rows to form an assessment matrix, and the assessment matrix can be stated as

Within the above matrix, n is the number of comment sets, and m is the number of assessment indicators.

Step3: Determine the weight vector of factors.

Weight is a value measuring the importance of assessment index factors. In this paper, the improved analytic hierarchy process and the entropy weight method are coupled to determine the weights of assessment factors. The weight set of assessment factors is represented as A = (Z1, Z2, Z3, ..., Zi), where Z ≫ 0, and ∑Z = 1, the calculation steps are as follows:

Calculate subjective weights of assessment factors with the improved analytic hierarchy process.

The standard deviation of the attribute values of the assessment factors is used instead of expert scoring to construct a judgment matrix. The sample standard deviation S(i) (i = 1 ∼ n) of each assessment index is used to reflect the degree of influence of the assessment index on the roof water inrush risk, and to construct the judgment matrix B. The calculation equation of b, which is the importance scale of the i-index relative to the j-index, is[39]

In the above, S(i) and S(j) are the sample standard deviations of index i and index j; Smax and Smin are the maximum and minimum values of {S(i)|i = 1∼n}; in b = min {9, int [Smax/Smin + 0.5]}, min, and int are the minimum and rounding functions.

To avoid inconsistency in the judgment matrix, it is necessary to check the consistency of the judgment matrix. The test equations are

CI is the consistency test index, RI is the random consistency ratio, and CR is the average random consistency index of the judgment matrix. When CR < 0.1, the judgment matrix satisfies the consistency test; otherwise, it needs to be adjusted until CR < 0.1, RI = 0.9.

According to the matrix, the eigenvector corresponding to the largest eigenvalue λmax is obtained, that is, the subjective weight vector: first calculate the product M of the elements in each row, then calculate the nth root u of M, and then normalize the vector u̅ = [u̅1, u̅2, u̅, ..., u̅]T to get the desired eigenvector u.

In the above, u is the weight of the improved AHP calculation; n is the order of the judgment matrix (the number of assessment indexes); λmax is the maximum eigenvalue.

Calculate the objective weight of assessment factors with the entropy weight method.

In Information Theory, entropy is a measure of the uncertainty of random variables, which can be used to measure the amount of information contained in the data itself and the degree of dispersion of the data. The more discrete the data, the smaller the information entropy, the greater the information deviation, the greater the amount of information contained, and the greater the impact on the output results. Therefore, the corresponding weight is greater, and vice versa.[51,56,57] The specific calculation is as follows:

The calculation equation of information entropy:

In the above, , x is the value of the jth factor in the ith group of data, where i = 1, 2, ..., n; j = 1, 2, ..., p.

The calculation equation of the weight w based on the information entropy

1 – E describes the information deviation degree of the jth factor. The larger the value, the higher the information content of the factor.

Determine the comprehensive weight of assessment indexes.

The subjective and objective weights of the assessment factors were brought into eq to obtain the comprehensive weight of the assessment factors;[48]

In eq , Z is the comprehensive weight of the assessment factor, u is the subjective weights of the assessment factor, and w′ is the objective weight of the assessment factor.

Step 4: Establishment of the IAHP-EWM roof water inrush risk assessment model.

Based on the assessment factor matrix and weight vector, a water inrush risk assessment model of working face is established:

In eqs and 15, B is a fuzzy subset of the comment set V, called the judgment set, B = (b1, b2, b3, ..., b), b is the membership degree of the jth assessment result in the assessment set of the assessment object; A is the weight vector; R is the assessment factor’s membership degree matrix.

The risk level of the evaluated object is determined according to the principle of maximum membership degree, which can be stated as follows: if A∈F(U) (i = 1, 2, ..., n) is defined, and u∈U, and i0 exists, then

A(u0) is the membership degree value.

Establishment of the Roof Water Inrush Risk Assessment Index System

This paper uses the multifactor and multi-index comprehensive assessment method. The main influencing factors of roof water inrush before and after drainage are determined through systematic analysis of the geological conditions and field measured data in Liangshuijing mining area, and the assessment index system of roof water inrush risk before and after drainage is established.

Assessment Index System of Water Inrush Risk before Drainage

When the coal seam roof is not drained, roof water inrush is affected by the combined effect of the water inrush source, water inrush channel, and mining. Therefore, considering these three factors, an assessment index system for the risk of water inrush before roof drainage is established, which includes 15 assessment indicators. Figure shows the assessment index system of water inrush risk of undrained water.

Figure 3

Assessment index system of water inrush risk of undrained water.

The factors in the system are as follows:

Water abundance of the aquifer (u1, u5): The stronger the water abundance of the aquifer, the higher the risk of water inrush from the roof during coal mining is. Quaternary loose sand phreatic water and fissure water of weathered bedrock of Jurassic Yan’an Formation are the main filling water sources during coal seam mining in the Liangshuijing coal mine. Both aquifers are considered. In this paper, the unit water inflow data (q) of boreholes are used to quantify the water abundance of aquifers.

The water head of the aquifer (u2, u6): The water head of the aquifer reflects the water-rich degree of the aquifer. The higher the water head in the aquifer, the higher the risk of water inrush from the roof is.

The permeability of the aquifer (u2, u4): The permeability coefficient represents the ability of the aquifer to transfer water. The larger the coefficient, the larger the water permeability of the aquifer, and the higher the risk of water inrush from the roof is.

Aquifer thickness (u3, u7): The aquifer is the source of roof water inrush. The thicker the aquifer, the greater the water content per unit thickness of the aquifer, and the higher the risk of roof water inrush.

Relief degree of land surface (gully area) (u9): The surface relief in the Liangshuijing mining area is relatively large, and there are valley areas in some sections. The valley area is a surface catchment area with strong water richness. When the mining fissure zone spreads to the surface, the water inrush threat to the working face is greater.

Fold undulation degree (u10): When predecessors established the roof water inrush index system, the influence of fold undulation degree on water inrush was not considered. In the actual mining process, the water inflow of the working faces is greatly affected by the fluctuation degree of folds. Figure shows the water inflow corresponding to fold relief. It can be seen from Figure that the variation degree of water inflow in the working face is basically the same as the fluctuation state of the fold. When the working face is recovered to the anticline position, the water inflow of the working face decreases. In the inclined position, the water inflow of the working face increases. The variation of water inflow of the working face is controlled by the degree of fold fluctuation. Therefore, when evaluating the risk of roof water inrush in the working face, the fluctuation degree of folds should be taken into account. Based on the concept of ″surface relief″ in surveying, ″Fold undulation degree (F)″ to quantify the relief shape of folds is proposed in this paper. The calculation equation is:[58]

Figure 4

Schematic diagram of water inflow corresponding to fold relief; when the working face is recovered to the fold anticline, the water inflow of the working face decreases, and when the working face is recovered to the fold syncline, the water inflow of the working face increases.

In eq , F represents the fold fluctuation degree, H represents the elevation value of the bottom surface of the aquifer, and Hmin represents the minimum elevation value of the bottom surface of the aquifer.

Height of the fractured water-conducting zone (u11): The higher the height of the fractured water-conducting zone is developed during coal mining, the more aquifers penetrate, and the greater the risk of water inrush.[59]

Aquifuge thickness (u13): The aquifuge has the function of blocking the hydraulic connection between the aquifer and the coal seam and preventing the development of cracks. The thicker the aquifuge thickness, the lower the risk of roof water inrush during coal mining.

Burial depth of coal seam (u14): The deeper the coal seam is buried, the more obvious the roof rock pressure is during coal seam mining, and the easier it is to cause water inrush accidents.

Coal seam thickness (u15): The greater the mining thickness of the coal seam, the stronger the disturbance to the overlying rock, the more serious the deformation and damage of the roof overlying rock, and the greater the risk of water inrush from the roof of the coal seam.

Assessment Index System of Water Inrush Risk after Drainage

Taking into account the influences of water inrush water source, water inrush channel, and drainage effect, an assessment index system of water inrush risk after drainage is established, which includes 13 assessment factors, as shown in Figure .

Figure 5

Assessment index system of roof water inrush after drainage.

The factors in the system are as follows:

Aquifer water head (u1′, u3′): The water head value of the aquifer indirectly reflects the water abundance of the aquifer. The higher the water head value after the aquifer is drained, the greater the risk of water inrush during coal mining.

Aquifer head attenuation degree (u2′, u4′): The water level attenuation rate of the aquifer reflects the degree of influence of the drilling drainage water on the aquifer. The higher the water level attenuation rate of the aquifer, the better the drainage effect is. The less the remaining water in the aquifer, the lower the risk of water inrush from the roof.

The initial water inflow of the borehole (u10′): The initial water inflow of the borehole reflects the water abundance of the aquifer. The larger the initial water inflow of the borehole, the better the water abundance of the aquifer and the greater the risk of water inrush from the roof.

The water inflow of the borehole (u11′): Borehole water inflow reflects the water abundance of the aquifer after the water is drained. The larger the borehole water inflow, the better the water abundance of the aquifer and the greater the risk of water inrush.

Attenuation rate of borehole water inflow (u12′): The attenuation rate of borehole water inflow reflects the drainage effect of water-draining boreholes. The greater the attenuation rate of the water inflow from the borehole, the better the drainage effect of the borehole, and the lower the risk of water inrush from the roof.

The volume of water drainage (u13′): Draining water volume reflects the effect of drilling drainage. The larger the drainage water volume, the better the drilling drainage effect. The lower the water storage capacity in the aquifer, the lower the risk of water inrush.

The assessment factors of relief degree of land surface (gully area) (u5′), fold undulation degree (u6′), height of the fractured water-conducting zone (u7′), and aquifuge thickness (u9′) are the same as those in Section 3.1, and repeated analysis will not be performed.

Through the analysis of the geological and hydrogeological data of the Liangshuijing coal mine, the assessment index suitable for the assessment of the water inrush risk of the roof of the 42205 working face is selected from the above index system. When the 42205 working face is not drained, the index set of the assessment index system is U1 = {the water abundance of weathered bedrock aquifer (u5), the water head of weathered bedrock aquifer(u6), weathered bedrock aquifer thickness (u7), the permeability of weathered bedrock aquifer (u8), fold undulation degree (u10), height of the fractured water-conducting zone (u11), aquifuge thickness (u13), buried depth of coal seam (u14), coal seam thickness (u15)}. The index set of roof water inrush risk assessment index system after drainage is U2 = {the water head of weathered bedrock aquifer (u3′), the attenuation rate of water head (u4′), fold undulation degree (u6′), height of the fractured water-conducting zone (u7′), aquifuge thickness (u9′), the initial water inflow of the borehole (u10′), the water inflow of the borehole (u11′), the attenuation rate of the borehole water inflow (u12′), and the volume of water drainage (u13′)}.

Results and Discussion

Establishing a Single-Factor Assessment Matrix Based on GIS

According to previous studies,[29,57,60−62] the roof water inrush risk level is divided into 5 levels. The water inrush risk comment set is V = {higher, high, medium, lower, low}. According to the drainage data of 17 exploration boreholes in the mining area and 66 boreholes in the 42205 working face, the assessment factors are collected and normalized through GIS, and a thematic map of assessment indicators is established. The membership of evaluation indicators can be determined from the thematic maps. Exploration drilling data is shown in Table . The drainage drilling data is shown in Table .

Table 1

Exploration Hole Data

hole number	water abundance of aquifer (L/S·m)	permeability of the aquifer (m/d)	thickness of the aquifer (m)	fold undulation degree (m)	height of fractured water-conducting zone (m)	aquifuge thickness (m)	burial depth of coal seam (m)	coal seam thickness (m)	water head of the aquifer (m)
IV-3	0.0362	0.21	11.70	51.30	52.00	23.20	115.3	3.33	30
IV-2	0.0337	0.24	11.90	35.30	53.70	14.60	60.10	3.45	21
BK1-3	0.0290	0.17	15.60	51.30	55.80	54.20	132.40	3.60	45
BK1-2	0.0288	0.22	12.30	40.30	55.00	36.10	103.30	3.54	35
K3-1	0.0414	0.28	11.50	28.30	51.50	22.90	118.20	3.30	35
K2-1	0.1936	1.18	15.90	47.70	40.50	2.80	50.30	2.52	2
K1-1	0.0428	0.34	10.70	20.30	60.70	16.70	89.40	3.95	3
K1-2	0.0185	0.21	9.40	27.30	64.20	28.30	103.90	4.20	10
II-2	0.0173	0.15	7.37	46.30	70.60	33.90	95.10	4.65	2
III-3	0.00149	0.01	10.60	45.30	41.80	29.80	140.10	2.61	19
LK14	0.0444	1.04	17.20	40.30	48.70	20.00	85.90	3.10	40
LK11	0.0421	0.39	16.50	34.30	46.60	13.90	93.50	2.95	27
LK5	0.0058	0.01	7.90	26.30	49.10	11.00	46.70	3.13	30
LK15	0.0338	0.24	17.80	67.30	43.10	52.30	159.40	2.70	29
LK8	0.0837	0.35	15.50	62.30	61.40	34.70	136.30	4.00	14
LK4	0.0396	0.22	9.60	32.30	48.76	19.90	83.10	3.10	25
LK19	0.0231	0.27	12.70	55.30	48.70	37.70	117.70	3.10	38

Table 5

Draining Water Drilling Data

hole number	u10′	u11′	u12′	u13′	hole number	u10′	u11′	u12′	u13′
q1-1	77.0	7	0.91	200.30	T5-3	81.0	4	0.95	16630
q1-2	72.0	7	0.90	42777.2	T5-4	blocked
q1-3	24.0	7	0.71	10619.1	T5-5	blocked
q1-4	27.5	7	0.75	32458.6	T5-6	blocked
q1-5	104.0	7	0.93	62713.3	T6-1	120	13	0.89	46283.6
q1-6	36.0	7	0.81	234442.3	T6-2	blocked
q2-7	36.0	10	0.72	34261.9	T6-3	139	13	0.91	59507.5
q2-8	30	10	0.67	40.0	T6-4	60	13	0.78	17732.0
q2-9	32.5	10	0.70	13624.6	T6-5	65	13	0.80	10418.8
q2-10	180.0	10	0.95	120718.1	T7-1	88	16	0.81	14726.6
q2-11	36.0	10	0.72	18834.0	T7-2	88	16	0.81	16930.5
q2-12	240.0	10	0.95	220859.0	T7-3	blocked
T1-1	89.0	6	0.93	100.1	T7-4	240	16	0.93	71228.6
T1-2	170.0	6	0.96	41975.8	T7-5	144	16	0.88	48487.6
T1-3	blocked				T8-1	103	5	0.95	29553.3
T1-4	blocked				T8-2	20	5	0.75	1202.1
T1-5	110.0	6	0.95	901.6	T8-3	60	5	0.91	20937.8
T1-6	blocked				T8-4	57	5	0.91	200.3
T2-1	blocked				T8-5	51	5	0.90	20236.5
T2-2	60.0	6	0.95	701.2	T9-1	blocked
T2-3	44.0	6	0.93	8615.5	T9-2	21	5	0.76	2203.9
T2-4	55.0	6	0.94	9617.3	T9-3	blocked
T3-1	blocked				T9-4	65	5	0.92	2183.9
T3-2	110.0	2	0.98	100.1	T9-5	55	5	0.91	28952.3
T3-3	115.0	2	0.98	100.1	T10-1	29	12	0.58	300.5
T3-4	52.0	2	0.96	11019.9	T10-2	60	12	0.80	27449.5
T4-1	blocked				T10-3	60	12	0.80	32057.9
T4-2	112.50	12	0.89	37868.4	T10-4	120	12	0.90	56001.1
T4-3	79.0	12	0.84	12021.7	T10-5	35	12	0.65	6712.1
T4-4	blocked				T10-6	blocked
T4-5	blocked				T11-1	103	12	0.88	157284.1
T5-1	108.0	4	0.96	8415.2	T11-2	100	40	0.60	37267.3
T5-2	128.0	4	0.96	6812.3	T11-3	120	40	0.66	166701.2

The water abundance of weathered bedrock aquifer (u5), the water head of weathered bedrock aquifer(u6), weathered bedrock aquifer thickness (u7), the permeability of weathered bedrock aquifer (u8), fold undulation degree (u10), height of the fractured water-conducting zone (u11), buried depth of coal seam (u14), coal seam thickness (u15), the water head of weathered bedrock aquifer (u3′), fold undulation degree (u6′), height of the fractured water-conducting zone (u7′), the initial water inflow of the borehole (u10′), and the water inflow of the borehole (u11′) are the benefit-type indicators, and eq is used to normalize the assessment factors. The attenuation rate of the borehole water inflow (u12′), and the volume of water drainage (u13′), aquifuge thickness (u13,u8′), the attenuation rate of water head (u4′), the attenuation rate of the borehole water inflow (u12′), and the volume of water drainage (u13′) belong to the cost-type indicators, and eq is used to normalize the assessment factors. Figures and 7 show the dimensionless thematic maps of assessment factors before and after drainage through ArcGIS. The thematic map of assessment factors is divided into five areas by the natural discontinuity method: {S1, S2, S3, S4, S5}, the membership degree of the assessment factors is determined by eq , and the assessment matrix is formed by determining the membership degree of each factor. R2 is the judging matrix of the 42205 working face before drainage, and R3 is the judging matrix of the 42205 working face after drainage.

Figure 6

Dimensionless thematic map of assessment factors before drainage of 42205 working face (calculation of the area ratio of classified areas to determine membership degree of each factor): (a) water abundance of the weathered bedrock aquifer, (b) permeability of weathered bedrock aquifer, (c) weathered bedrock aquifer thickness, (d) water head of weathered bedrock aquifer, (e) fold undulation degree, (f) height of water-conducting fracture zone, (g) aquifuge thickness, (h) buried depth of coal seam, and (i) coal seam thickness.

Figure 7

Dimensionless thematic map after drainage of 42205 working face (calculation of the area ratio of classified areas to determine membership degree of each factor): (a) water head of the weathered bedrock aquifer, (b) attenuation rate of the water table (weathered bedrock aquifer), (c) fold undulation degree, (d) height of the fractured water-conducting zone, (e) aquifuge thickness, (f) initial water inflow of the borehole, (g) water inflow volume of the borehole, (h) attenuation rate of the borehole water inflow volume, and (i) volume of water drainage.

Assessment Factor Weight Vector Set

Weight Vector before Drainage

Table shows the standard deviation of assessment factors before drainage. The subjective weights of assessment factors before drainage are obtained by substituting standard deviation data of assessment factors into eqs , 2, 9, and 10. Table shows the attribute value of assessment factors before drainage. The objective weights of assessment factors before drainage are calculated by substituting the attribute values of evaluation factors into eqs and 14. The comprehensive weights of assessment factors before drainage are obtained by substituting subjective weights and objective weights into eq , and Table shows the weight calculation results of each assessment factors.

Table 2

Standard Deviation of Influencing Factors

factors	u5	u8	u7	u10	u11	u13	u14	u15	u6
standard deviation	0.0020	0.0120	1.9130	3.7740	0.4320	6.4060	15.8810	0.0300	3.2110

Table 3

Composite Weights of Influencing Factors

factors	u5	u8	u7	u10	u11	u13	u14	u15	u6
subjective weight	0.1787	0.1782	0.1067	0.0680	0.1573	0.0415	0.0143	0.1772	0.0776
objective weight	0.2710	0.3080	0.0279	0.0412	0.0090	0.1183	0.0415	0.0119	0.1702
composite weights	0.3684	0.4174	0.0226	0.0213	0.0116	0.0373	0.0045	0.0160	0.1005

From Table , the assessment weight vector A1 can be obtained before drainage.

A1 = (0.3684, 0.4174, 0.0226, 0.0213, 0.0116, 0.0373, 0.0045, 0.0160, 0.1005).

Weight Vector after Drainage

Table shows standard deviation of assessment factors after drilling drainage. The subjective weights of assessment factors after drainage are obtained by substituting standard deviation data of assessment factors into eqs , 2, 9, and 10. Tables and 6 show the attribute value of assessment factors after drainage The objective weights of assessment factors after drainage are calculated by substituting the attribute values of evaluation factors into eq and 14. The comprehensive weights of assessment factors after drainage are obtained by substituting subjective weights and objective weights into eq , and Table shows the weight calculation results of each assessment factors.

Table 4

Standard Deviation of Influencing Factors after Drilling Drainage

factors	u3′	u4′	u6′	u7′	u8′	u10′	u11′	u12′	u13′
standard deviation	2.1320	0.0643	3.7744	0.4322	6.4066	16.0385	2.8041	0.0485	22636.87

Table 6

Water Head of the Aquifer in the Working Face

no.	u3′	u4′
1	7.9	0.72
2	6.5	0.77
3	5.1	0.83
4	6.2	0.80
5	11.1	0.66
6	8.1	0.76
7	3.7	0.89
8	7.5	0.79
9	12.2	0.67
10	9.3	0.75
11	6.1	0.83
12	5.8	0.84

Table 7

Assessment Factor Weights

factors	u3′	u4′	u6′	u7′	u8′	u10′	u11′	u12′	u13′
subjective weight	0.9803	0.9985	0.9999	0.9998	0.9969	0.9581	0.9416	0.9978	0.8226
objective weight	0.1233	0.1234	0.1232	0.1234	0.1231	0.1228	0.1233	0.1234	0.0137
composite weights	0.1335	0.0101	0.0067	0.0013	0.0209	0.2828	0.3958	0.0149	0.1335

From Table , the weight vector A2 of each factor after the drainage can be determined.

A2 = (0.1335, 0.0101, 0.0067, 0.0013, 0.0209, 0.02828, 0.3958, 0.0149, 0.1335).

Water Inrush Risk Assessment before and after the Drainage in the 42205 Working Face

Risk Assessment of Water Inrush before Drainage

Based on the pre-drainage assessment factor assessment membership matrix and assessment factor weight vector obtained in Sections and 4.2, the water inrush risk assessment model for the 42205 working face before drainage is established as follows:

In eq , B1 is the assessment vector of the risk of water inrush before the drainage of the working face, A1 is the weight vector of the assessment factors before the drainage, and R2 is the assessment membership matrix of the assessment factors before the drainage.

After calculation, B1 = A1 × R1 = (0.1898, 0.2269, 0.1979, 0.1852, 0.2000). According to the maximum membership criterion to determine the water inrush risk level of the working face, 0.2269 is ″high″ in the corresponding comment set; thus, the roof water inrush risk assessment level before water discharge is high.

Risk Assessment of Water Inrush after Drainage

Based on the pre-evacuation assessment factor assessment matrix and assessment factor weight vector obtained in Sections and 4.2.2, the water inrush risk assessment model for the 42205 working face after water evacuation is established as follows:

In eq , B2 is the assessment vector of the risk of water inrush before the drainage of the working face, A2 is the weight vector of the assessment factors before the drainage, and R3 is the assessment membership matrix of the assessment factors after the drainage:

After calculation, B2 = A2 × R3 = (0.1178, 0.1507, 0.2176,0.2836, 0.2303). The water inrush risk level of the working face is determined according to the maximum membership criterion. 0.2836 is “low” in the corresponding comment set; therefore, the roof water inrush risk assessment level is low after the drainage.

Verification of Assessment Results

Geophysical detection results are often used to determine the dangerous area for the occurrence of roof water inrush in the Liangshuijing mining area, and the water inrush risk of the working face is determined by the proportion of the dangerous area. Figure shows the regional distribution of geophysical dangerous areas in the aquifer. The geophysical prospecting staff delineated the danger area for roof water inrush. It can be seen from Figure a that when the working face is not drained, the dangerous area is large and accounts for 50% of the total area of the working face. The risk level is high when the working face is mined. It can be seen from Figure b that when the working face is drained, the dangerous area is significantly reduced, and accounts for 10% of the total area of the working face. The risk level is low. Comparison of the aquifer resistivity changes before and after drainage shows that the drainage effectively reduced the risk of water inrush in the working face. The evaluation results are as follows: the risk level is ’high’ when the working face is not drained, which is consistent with the geophysical detection results. The risk level is ’low’ when the working face is drained, which is consistent with geophysical detection results as well. From May 2021 to November 2021, during the actual mining process of the working face, there was no water inrush accident on the roof of the working face, the water inflow of the working face was small, and the average water inflow was maintained at 20 m3/h. Therefore, the results of geophysical exploration and the field measurement data of the working face show that the IAHP-EWM assessment model based on GIS can reliably assess the risk of water inrush before and after drainage of the working face.

Figure 8

Regional distribution of geophysical water inrush dangerous areas in the roof aquifer of the 42205 working face; (a) undrained water of the 42205 working face; (b) drained water of the 42205 working face (the roof water inrush risk area of the working face is large before drainage, and the roof water inrush risk area is small after drainage).

More Applications

To further verify the applicability of the IAHP-EWM model, the risk levels of water inrush for the 42202, 42203, and 42204 working faces after water are assessed. A period of 1 month of dredging water is applied in the 42204 working face, and the amount of drainage water is 160,000 m3. A period of 6 months of dredging water is applied in the 42203 and 42204 working faces, and the amount of drainage water are 640,000 and 800,000 m3, respectively. The assessment results show that the water inrush risk level of the 42204 working face is ‘extremely high’ after the water is dredged and the mining conditions are not met while the results for the 42202 and 42203 working faces are both ‘low’ and meet the safe mining conditions. Figures –11 show the assessment factors’ dimensionless thematic maps after drainage for the 42202, 42203, and 42204 working faces. Figure shows the regional distribution of geophysical water inrush dangerous areas in the roof aquifer. It can be seen from Figure that the area of water inrush risk (water-rich area) of the roof aquifer is relatively large. If mining is carried out at this time, the risk of water inrush at the working face is high, and roof drainage needs to be strengthened. The data of water inflow during the actual mining process of the 42203 and 42204 working faces show that no water inrush accident occurred in the mining face after the water was dredged, and the water inflow of the working face was low and the average water inflow was maintained at 30 and 20 m3/h. There is no risk of water inrush in the mining of the working face. Therefore, the assessment results of the 42202, 42203, and 42204 working faces show that the IAHP-EWM model based on GIS can effectively evaluate the risk of roof water inrush after the working face is drained. The model can be used to provide a reference for the assessment of the risk of water inrush at the working face before and after drainage in the Liangshuijing mining area and the effect of drainage.

Figure 9

Dimensionless thematic map after drainage of the 42202 working face (calculation of the area ratio of classified areas to determine membership degree of each factor): (a) water head of the weathered bedrock aquifer, (b) attenuation rate of the water table (weathered bedrock aquifer), (c) fold undulation degree, (d) height of the fractured water-conducting zone, (e) aquifuge thickness, (f) initial water inflow of the borehole, (g) water inflow volume of borehole, (h) attenuation rate of the borehole water inflow volume, and (i) volume of water drainage.

Figure 11

Regional distribution of geophysical water inrush dangerous areas in the roof aquifer of the 42204 working face.

Figure 12

Dimensionless thematic map after drainage of the 42204 working face (calculation of the area ratio of classified areas to determine membership degree of each factor): (a) water head of the weathered bedrock aquifer, (b) attenuation rate of the water table (weathered bedrock aquifer), (c) fold undulation degree, (d) height of the fractured water-conducting zone, (e) aquifuge thickness, (f) initial water inflow of the borehole, (g) water inflow volume of the borehole, (h) attenuation rate of the borehole water inflow volume, and (i) volume of water drainage.

Dimensionless thematic map after drainage of the 42203 working face (calculation of the area ratio of classified areas to determine membership degree of each factor): (a) water head of the weathered bedrock aquifer, (b) attenuation rate of the water table (weathered bedrock aquifer), (c) fold undulation degree, (d) height of the fractured water-conducting zone, (e) aquifuge thickness, (f) initial water inflow of the borehole, (g) water inflow volume of the borehole, (h) attenuation rate of the borehole water inflow volume, and (i) volume of water drainage.

Conclusions

A multifactor index system is established for evaluating the risk of water inrush from the roof of the working face before and after drainage. The introduction of the ″fold undulation degree (F)″ index quantifies the influence of the fold spatial positional undulation shape on water inrush.

A mathematical model of roof water inrush risk assessment (IAHP-EWM) is established and applied to evaluate the water inrush risk of the 42205 working face. The results show that the water inrush risk level is “high”; the working face is mined after the water is dredged, and the water inrush risk level is “low”. The applicability and effectiveness of the model are verified by the results of geophysical exploration and the actual water inflow in the mining face.

The model is extended and applied to the 42202, 42203, and 42204 working faces. The results show that the model can provide a reference for the assessment of the risk of water inrush at the working face before and after drainage in the Liangshuijing mining area and the effect of drainage.

Further Study

The hydrochemical influence of the aquifer cannot be ignored in the evaluation of water inrush risk. In the future, the hydrochemical characteristics of groundwater should also be taken into account in the selection of evaluation indexes.[63−65]

Table 8

Assessment Factor Entropy Value

factors	u5	u8	u7	u10	u11	u13	u14	u15	u6
information entropy	0.8904	0.8755	0.9887	0.9833	0.9960	0.9521	0.9831	0.9951	0.9312

Table 9

Composite Weights of Influencing Factors

factors	u5	u8	u7	u10	u11	u13	u14	u15	u6
subjective weight	0.1787	0.1782	0.1067	0.0680	0.1573	0.0415	0.0143	0.1772	0.0776
objective weight	0.2710	0.3080	0.0279	0.0412	0.0090	0.1183	0.0415	0.0119	0.1702
composite weights	0.3684	0.4174	0.0226	0.0213	0.0116	0.0373	0.0045	0.0160	0.1005

Table 10

Assessment Factor Entropy Value

factors	u3′	u4′	u6′	u7′	u8′	u10′	u11′	u12′	u13′
information entropy	0.9803	0.9985	0.9999	0.9998	0.9969	0.9581	0.9416	0.9978	0.8226

Table 11

Composite Weights of Influencing Factors

factors	u3′	u4′	u6′	u7′	u8′	u10′	u11′	u12′	u13′
subjective weight	0.9803	0.9985	0.9999	0.9998	0.9969	0.9581	0.9416	0.9978	0.8226
objective weight	0.1233	0.1234	0.1232	0.1234	0.1231	0.1228	0.1233	0.1234	0.0137
composite weights	0.1335	0.0101	0.0067	0.0013	0.0209	0.2828	0.3958	0.0149	0.1335