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Prediction of subsidence due to underground mining by artificial neural networks
Ambrožič, Tomaž (Author), Turk, Goran (Author)

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Abstract
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.

Language:English
Keywords:artificial neural network, subsidence prediction, multi-layer feed=forward neural network, approximation of functions, mining damage
Tipology:1.01 - Original Scientific Article
Organization:FGG - Faculty of Civil and Geodetic Engineering
Year:2003
Publisher:Elsevier
Number of pages:str. 627-637
Numbering:Vol. 29, Vol. 29
UDC:624.131.5
ISSN on article:0098-3004
DOI:10.1016/S0098-3004(03)00044-X Link is opened in a new window
COBISS.SI-ID:1972833 Link is opened in a new window
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Downloads:548
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Record is a part of a journal

Title:Computers & Geosciences
Shortened title:Comput. geosci.
Publisher:Pergamon Press
ISSN:0098-3004
COBISS.SI-ID:25264128 This link opens in a new window

Secondary language

Language:English
Keywords:umetna nevronska mreža, napovedovanje ugreznine, večslojna usmerjena nevronska mreža, aproksimacija funkcij, rudarska škoda

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