Evaluation of the Water Yield of Coal Roof Aquifers Based on the FDAHP-Entropy Method: A Case Study in the Donghuantuo Coal Mine, China

Abstract

The water yield of coal seam roof aquifers is the key factor for evaluating and controlling water disasters in coal seam roofs. To evaluate the water yield of the sandstone aquifer in the roof of the Carboniferous-Permian Damiaozhuang Formation no. 8 coal seam in the Donghuantuo Mine, North China, seven main controlling factors affecting the water yield of sandstone aquifers are determined, including the permeability coefficient, consumption of drilling fluid, core recovery, aquifer thickness, brittle-plastic rock thickness ratio, fault scale index, and fault point density. Further, the fuzzy Delphi analytic hierarchy process (FDAHP) and entropy weight method (EWM) are used to calculate the subjective and objective weights of each main factor, respectively, and a combination weight model (CWM) is proposed based on the least square method to compose the comprehensive weights. Then, an improved water yield property index (IWYPI) model is established, and the water yield zoning map of sandstone aquifers is acquired. Engineering practice shows that the evaluation accuracy of the water yield property index (IWYPI) model based on the CWM is as high as 93.75%, which is 18.75% and 12.5% higher than that of the water yield property index (WYPI) model based on the FDAHP and EWM, respectively. The research results propose a novel method for evaluating the water yield of coal seam roof aquifers and can provide scientific guidance for the prevention and control of water disasters in the no. 8 coal seam roof of the Donghuantuo Coal Mine.

Multifactor Prediction of the Water Richness of Coal Roof Aquifers Based on the Combination Weighting Method and TOPSIS Model: A Case Study in the Changcheng No. 1 Coal Mine

“…The more developed the faults and folds, the larger the WRI value and the greater the water richness. As shown in Figures 9,10,and 11, the distribution of faults and folds is predominantly in a northwest direction, and the distribution directions of moderate and strong water richness zones based on the WRI are also in a northwest direction, as shown in Figure 18, indicating that the distribution of water richness zones is controlled by the distribution of faults and folds. With a weight of 0.155, the CR factor, which reflects the integrity of a rock mass, plays an important role in the connectivity of groundwater, which is advantageous to the groundwater potential.…”

“…Many scholars have conducted studies that consider multiple factors to assess the water richness of the roof aquifer. Common methods include the entropy weight method (EWM), water-rich index model based on the GIS information fusion principle, backpropagation (BP) neural network method, fuzzy clustering comprehensive evaluation method, analytic hierarchy process (AHP), variation coefficient method, triangular fuzzy number (TFN), and fuzzy Delphi analytic hierarchy process (FDAHP). − The main idea of establishing the multifactor evaluation model of roof aquifer water richness is to establish the index system of aquifer water richness influencing factors and then employ one or more methods to determine the relative weight of various factors. However, the existing comprehensive evaluation methods have limitations in the evaluation process.…”

Application of Space–Sky–Earth Integration Technology with UAVs in Risk Identification of Tailings Ponds

“…The United States launched its first Landsat satellite in 1972, marking the beginning of the space remote sensing era [12]. Remote sensing technology started later in the field of mining in China than in other countries, but the quality of remote sensing data has developed synchronously [13], and Landsat technology has been increasingly applied to the remote sensing of wind, clouds, resources, and the environment, along with the Gaofen (GF) series. The stable operation of the satellite series is represented by a space-Earth observation system.…”

Evaluation of mining capacity of mines using the combination weighting approach: A case study in Shenmu Mining Area in Shaanxi Province, China

“…The digital features of Ex, En, and He reflect the overall characteristics of the cloud model, which are expressed as expectation, entropy, and superentropy, respectively. [37][38][39] Weights of each index are determined by using the combination weighting approach combining the improved AHP with EWM, which can not only learn theories and experience from senior experts but also reduce subjective arbitrariness of weighting so that evaluation results are more authentic and reliable. Note that senior experts come from coal mines or government departments.…”