Journal
COMPUTERS & GEOSCIENCES
Volume 29, Issue 5, Pages 627-637Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/S0098-3004(03)00044-X
Keywords
subsidence prediction; artificial neural network; multi-layer feed-forward neural network; approximation of functions; mining damage
Ask authors/readers for more resources
Alternatively to empirical prediction methods, methods based on influential functions and on mechanical model, artificial neural networks (ANNs) can be used for the surface subsidence prediction. In our case, the multi-layer feed-forward neural network was used. The training and testing of neural network is based on the available data. Input variables represent extraction parameters and coordinates of the points of interest, while the output variable represents surface subsidence data. After the neural network has been successfully trained, its performance is tested on a separate testing set. Finally, the surface subsidence trough above the projected excavation is predicted by the trained neural network. The applicability of ANN for the prediction of surface subsidence was verified in different subsidence models and proved on actual excavated levels and in levelled data on surface profile points in the Velenje Coal Mine. (C) 2003 Elsevier Science Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available