WU H，CUI D W，ZHANG J,et al．Research on the prediction of the development height of water-conducting fractured zones in coal mines based on the XGBoost algorithm［J］.Journal of Henan Polytechnic University( Natural Science) ，doi:10.16186/j.cnki.1673-9787. 2024090024

doi: doi:10.16186/j.cnki.1673-9787. 2024090024

Received：2024-09-11

Revised：2025-04-25

Online：2025-05-07

Research on the prediction of the development height of water-conducting fractured zones in coal mines based on the XGBoost algorithm

WU Hong¹，CUI Dengwei¹，ZHANG Jitao², DING Heng³

(1. 106 Geological Group, Guizhou Bureau of Geology and Mineral Resources Exploration and Development, Zunyi 563000, Guizhou, China;2. Guizhou Qianchenglijin Technology Co., Ltd., Guiyang 550081,Guizhou,China;3. Guizhou Institute of Geological Environment Monitoring, Guiyang 550081, Guizhou, China)

Abstract: [Objective] In order to accurately predict the development height of the water-conducting fractured zone in coal mines, the mining depth H, mining thickness M, working face length l, and the proportion b of hard rock lithology in the overlying strata are selected as the main influencing factors for the development of the water-conducting fractured zone. [Methods] Establish a height prediction model of the water-conducting fractured zone based on the XGBoost algorithm, and select the coefficient of determination (R2) and the mean absolute percentage error (MAPE) as the model evaluation indicators. [Results] The optimal tree depth of XGBoost is 8, and the optimal number of trees is 250. Compared with other models, under this parameter, the XGBoost model has a larger R2 and a smaller MAPE on the training set and test set, and the model is superior to other models. The parameters of the working face of Zhaogu No. 2 Coal Mine are selected to calculate the development height of the water-conducting fracture zone, and the predicted results of the XGBoost model are compared with the measured results. By comparing the differences between the predicted values of the XGBoost model, similar simulations, theoretical calculations, and the measured height of the water-conducting fracture zone at the working face, the relative error and absolute error between the predicted value and the measured value of the XGBoost model are relatively small, which is better than similar simulations and theoretical calculations. The model error is within the allowable error range of the project, and the XGBoost model can be used to predict the height of the water-conducting fracture zone in the follow-up. [Conclusion] The results of this research provide valuable insights for accurately predicting the development height of the water-conducting fracture zone in coal mines, offering a reliable tool for practical applications.

Key words: water-conducting fracture zone; XGBoost algorithm; integrated learning; extreme gradient;analog simulation

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