Abstract
Six main influencing factors: slope, aspect, distance, angle, angle of coal seam, and the ratio of depth and thickness, were selected by Grey correlation theory and Grey relational analysis procedure programmed by the MATLAB software package to select the surface movement and deformation parameters. On this basis, the paper built a BP neural network model that takes the six main influencing factors as input data and corresponding value of ground subsidence as output data. Ground subsidence of the 3406 mining face in Haoyu Coal was predicted by the trained BP neural network. By comparing the prediction and the practices, the research shows that it is feasible to use the BP neural network to predict mountain mining subsidence.
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Zhang, Hs., Liu, Lj. & Liu, Hf. Mountain ground movement prediction caused by mining based on BP-neural network. J Coal Sci Eng China 17, 12–15 (2011). https://doi.org/10.1007/s12404-011-0103-7
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DOI: https://doi.org/10.1007/s12404-011-0103-7