DocumentCode :
1900115
Title :
Notice of Retraction
Application of D-S Evidence Theory in Mine Water--Inrush from the Floor
Author :
Xin Heng-qi ; Shi Long-qing ; Yu Xiao-ge ; Han Jin ; Yang Fengjie
Author_Institution :
Sch. of Geol. Sci. & Eng., Shandong Univ. of Sci. & Technol., Qingdao, China
fYear :
2010
fDate :
25-26 Dec. 2010
Firstpage :
1
Lastpage :
5
Abstract :
Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

Based on summing up forecasting method and theory about water-inrush from floor of working face, combined with a lot of practical information analysis, the main five factors controlling water-inrush from floor are found out, which are water yield property of aquifer, water pressure, effective confining stratum thickness of mining floor, fault or broken zone and mining pressure. According to the typical water-inrush cases in Feicheng coal field, forecast of water-inrush from floor based on neural networks and D-S evidence theory is built. Different state confidence of mining workface is got by Neural Networks and experts points which as the evidence body. Other coal fields in Feicheng is forecast according to the established model. By comparing the result of model and the result of water bursting coefficient with the actual result, the result obtained by model is closer to reality than the result of water bursting coefficient, which can be meet the need of project. The conclusion is got that the forecast of water-inrush from floor based on neural networks and D-S Evidence theory is not only has important theoretical basis, but also has practical application value.
Keywords :
coal; forecasting theory; inference mechanisms; information analysis; mining industry; neural nets; production engineering computing; D-S evidence theory; Feicheng coal field; forecasting method; information analysis; mine water-inrush; neural networks; water bursting coefficient; water yield property; Artificial neural networks; Floors; Geology; Safety; Training; Uncertainty; Water;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Engineering and Computer Science (ICIECS), 2010 2nd International Conference on
Conference_Location :
Wuhan
ISSN :
2156-7379
Print_ISBN :
978-1-4244-7939-9
Type :
conf
DOI :
10.1109/ICIECS.2010.5678303
Filename :
5678303
Link To Document :
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