Prediction of subsidence due to underground mining by artificial neural networks

Tomaž Ambrožič (Author), Goran Turk (Author)

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.

Keywords

umetna nevronska mreža;napovedovanje ugreznine;večslojna usmerjena nevronska mreža;aproksimacija funkcij;rudarska škoda;artificial neural network;subsidence prediction;multi-layer feed=forward neural network;approximation of functions;mining damage;

Data

Language:	English
Year of publishing:	2003
Typology:	1.01 - Original Scientific Article
Organization:	UL FGG - Faculty of Civil and Geodetic Engineering
Publisher:	Elsevier
UDC:	624.131.5
COBISS:	1972833
ISSN:	0098-3004
Views:	2870
Downloads:	1074
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	English
Secondary keywords:	umetna nevronska mreža;napovedovanje ugreznine;večslojna usmerjena nevronska mreža;aproksimacija funkcij;rudarska škoda;
File type:	application/pdf
Type (COBISS):	Not categorized
Pages:	str. 627-637
Volume:	ǂVol. ǂ29
Chronology:	2003
DOI:	10.1016/S0098-3004(03)00044-X
ID:	8312291