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Coal Engineering ›› 2026, Vol. 58 ›› Issue (3): 198-205.doi: 10.11799/ce202603024

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Roof flooding risk assessment method based on the fusion of AHP-entropy weight method and KNN machine learning

  

  • Received:2025-04-07 Revised:2025-07-03 Online:2026-03-10 Published:2026-04-14

Abstract:

The water inrush risk assessment method is the theoretical basis for the design and optimization of mine production and drainage system. In order to construct a more scientific and rigorous risk assessment system for coal seam roof water inflow, the AHP (analytic hierarchy process)-entropy weight method was used to optimize the comprehensive weight of the influencing factors of water inflow, and the main controlling factors of coal seam roof water inflow were determined. Based on the sample data of six indexes, including permeability coefficient, aquifer thickness, pore water pressure, temperature and cohesion, a coal seam roof water inrush evaluation model was trained with the logical framework of "data input-model output-field verification-performance improvement-simulation prediction". The results show that the thickness of the aquifer predicted by the KNN model has the greatest impact on the water inflow of the roof of the Galutu coal seam, followed by the permeability coefficient. The accuracy of the KNN prediction model is 0.9598 and F1-score: 0.9569, which verifies the rationality of the selection of the evaluation index of coal seam water inrush risk and the feasibility of the model prediction. It is predicted that the auxiliary transportation of 2105 working face will be in the high-risk area in the range of 0~330m and 1620~2210m from the cutting hole of the working face, and the research results have expanded the risk assessment method of underground coal seam water inflow.

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