A Neural Network based Intelligent Method for Mine Slope Surface Deformation Prediction Considering the Meteorological Factors

Sunwen Du, Jin Zhang, Zengbing Deng, Jingtao Li


Accurate mine slope surface deformation forecasting can provide reliable guidance for safe mining production and mine construction planning, which is also important for the personnel safety of the mining staffs. The mine slope surface deformation forecasting is a non-linear problem. Generalized regression neural network (GRNN) has been proven to be effective in dealing with the non-linear problems, but it is still a challenge of how to determine the appropriate spread parameter in using the GRNN for deformation forecasting. In this paper, a mine slope surface deformation forecasting model combining artificial bee colony optimization algorithm (ABC) and generalized regression neural network was proposed to solve this problem. The effectiveness of this proposed forecasting model was proved by experiment comparisons. The test results show that the proposed intelligent forecasting model outperforms the BP neural network forecasting model, BP neural network with genetic algorithm optimization (GA-BPNN) and the ordinary linear regression (LR) forecasting models in the mine slope surface deformation forecasting.


DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4815


slope deformation prediction; generalized regression neural network; artificial bee colony algorithm; optimization problem; parameter selection

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