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

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PID: 20.500.12556/rul/7a5bcf5a-86c0-49c7-ae6b-5b549fc1545a

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.

Keywords:artificial neural network, subsidence prediction, multi-layer feed=forward neural network, approximation of functions, mining damage
Typology:1.01 - Original Scientific Article
Organization:FGG - Faculty of Civil and Geodetic Engineering
Number of pages:Str. 627-637
Numbering:Vol. 29, Vol. 29
PID:20.500.12556/RUL-32124 This link opens in a new window
ISSN on article:0098-3004
DOI:10.1016/S0098-3004(03)00044-X This link opens in a new window
COBISS.SI-ID:1972833 This link opens in a new window
Publication date in RUL:10.07.2015
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Record is a part of a journal

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

Secondary language

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

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