Risk Assessment and Zonation of Roof Water Inrush Based on the Analytic Hierarchy Process, Principle Component Analysis, and Improved Game Theory (AHP–PCA–IGT) Method

Abstract

With the large-scale mining of deeply buried coal seams, the risk of roof water inrush increases during mining. In order to ensure safe mining, it is necessary to predict the risk potential of water inrush from the roof aquifer. This study introduces a coupling evaluation method, including the analytic hierarchy process (AHP), principal component analysis (PCA), and improved Game theory (IGT). This paper takes the water inrush from the roof aquifer of the 11-2 coal seam in Kouzidong mine as the research object. An evaluation index system is constructed by selecting six evaluation factors, including the aquitard effective thickness, aquiclude thickness, the ratio of sandstone to mudstone, rock quality designation, fault fractal dimension, and wash water quantity of geological log. The comprehensive weighting method based on IGT is used to optimize the subjective and objective weighting values obtained by AHP and PCA methods in turn, and an AHP–PCA–IGT evaluation model is established to divide and evaluate the water inrush risk zonation of the roof aquifer. The risk degree of the water inrush gradually decreases from the center to the north–south, and the main areas with relatively high risks and higher risks are distributed in a small part of the western and eastern regions. Finally, combining various drilling data examples, drilling pumping tests, and water inrush sites, the accuracy of the predicted results is validated through the vulnerability fitting percentage (VFP). The predictions are basically consistent with the actual results, and this study lays a theoretical foundation for the prevention and control of water inrush hazards.

Local Water Inrush Risk Assessment Method Based on Moving Window and Its Application in the Liangshuijing Mining Area

“…The Formations (Fmts) in the study area, from oldest to youngest, are as follows: Yongping Fmt (T3y), Yan'an Fmt (J2y), Zhiluo Fmt (J2z), Xinjiang Fmt (N2b), Lishi Fmt (Q2l), Salawusu Fmt (Q3 s ), and Quaternary Holocene Aeolian Sand (Q4 eol ). Figure 2 presents the hydrogeological cross-section of the study area [18][19][20][21][22]. The geological structure of the study area is undeveloped, with gentle strata and a dip angle of less than 1 • .…”

“…For assessing roof water inrush risk in coal mines, seven evaluation indicators were selected [17,21]: water abundance of aquifer (WWA), aquifer permeability (AP), aquifer thickness (At), aquifuge thickness (AFT), coal seam thickness (CT), mining depth (MD), overburden failure height (OFH). The rationale behind the selection of each evaluation indicator is outlined below.…”

“…In this study, the thickness data of aquifers at borehole locations in the study area were collected, and the study area zoning map of AT was generated using the ArcGIS Kriging interpolation method (Figure 3b). For assessing roof water inrush risk in coal mines, seven evaluation indicators were selected [17,21]: water abundance of aquifer (WWA), aquifer permeability (AP), aquifer thickness (At), aquifuge thickness (AFT), coal seam thickness (CT), mining depth (MD), overburden failure height (OFH). The rationale behind the selection of each evaluation indicator is outlined below.…”

“…After undergoing consistency testing with the AHP, 259 sets of qualified weight samples were obtained. Figure 8 depicts the probability density estimation of the criterion weight samples obtained through the MAHP method, while Figure 9 displays the distribution functions of the weights for the evaluation criterion [21]. Table 1 provides statistical data on criterion weights, with values in parentheses denoting the 95% confidence interval of the mean weights.…”

“…However, these evaluation methods share a common issue in the allocation of weights to evaluation indicators. Specifically, they assign the same weight values to evaluation indicators across all locations within the study area [14][15][16][17][18][19][20][21][22][23]. This implies that existing evaluation methods do not account for the varying relative importance of evaluation indicators at different locations within the study area, resulting from differences in the magnitude of attribute values.…”