WANG Xu，YIN Shangxian，XU Bin，et al. Study on height optimization prediction model of overburden water-conducting fracture zone under fully mechanized mining[J]. Coal Science and Technology，2023，51（S1）：284−297
. DOI: 10.13199/j.cnki.cst.2022-1530| Citation: |
WANG Xu，YIN Shangxian，XU Bin，et al. Study on height optimization prediction model of overburden water-conducting fracture zone under fully mechanized mining[J]. Coal Science and Technology，2023，51（S1）：284−297 . DOI: 10.13199/j.cnki.cst.2022-1530
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Accurately predicting the height of water-conducting fracture zones is an important issue in coal mine water control and a prerequisite for ensuring mine safety. Currently, there are problems with the accuracy of prediction models for water-conducting fracture zone height. Based on collecting a wide range of measured data on water-conducting fracture zone development height in various coal mining areas and summarizing previous research achievements, a prediction model for water-conducting fracture zone height was established, taking into account five factors: mining thickness, face elongation, dip angle, mining depth, and proportion coefficient of hard rock.Factor analysis was used to analyze the weights of influencing factors and determine the dominant factor. Multiple linear regression model, multiple nonlinear regression model, and backpropagation (BP) neural network prediction model were established. Accuracy tests were conducted on the prediction models to calculate the height of water-conducting fracture zones. Using measured data from the Xishan mining area as an example, the optimal model was selected, and the BP neural network model, as the optimal prediction model, was analyzed and compared with the “Sanxia” empirical formula, multiple linear regression model, and multiple nonlinear regression model in terms of the computed results The results showed that the “San xia” empirical formula had distortions compared to actual data and could no longer guide the prediction of water-conducting fracture zone height under comprehensive mining conditions. On the other hand, the BP neural network prediction model had an error of less than 10%, with stable absolute and relative errors. Additionally, this model reduced the correlation between factors and improved prediction accuracy. Therefore, the BP neural network prediction model demonstrates good accuracy and applicability in predicting water-conducting fracture zone height. The research findings can provide reference suggestions for guiding on-site water hazard prevention and control in mines.
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