Prediction of ground subsidence in Samcheok City,Korea using artificial neural networks and GIS |
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| Authors: | Ki-Dong Kim Saro Lee Hyun-Joo Oh |
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| Institution: | (1) Department of Geoinformation Engineering, Sejong University, 98 Gunja-dong, Gwangjin-gu, Seoul, 143-747, South Korea;(2) Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources (KIGAM), 92 Gwahang-no, Yuseong-gu, Daejeon, 305-350, South Korea;(3) Department of Earth System Sciences, Yonsei University, 262 Seongsanno, Seodaemun-gu, Seoul, 120-749, South Korea |
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| Abstract: | This study shows the construction of a hazard map for presumptive ground subsidence around abandoned underground coal mines
(AUCMs) at Samcheok City in Korea using an artificial neural network, with a geographic information system (GIS). To evaluate
the factors governing ground subsidence, an image database was constructed from a topographical map, geological map, mining
tunnel map, global positioning system (GPS) data, land use map, digital elevation model (DEM) data, and borehole data. An
attribute database was also constructed by employing field investigations and reinforcement working reports for the existing
ground subsidence areas at the study site. Seven major factors controlling ground subsidence were determined from the probability
analysis of the existing ground subsidence area. Depth of drift from the mining tunnel map, DEM and slope gradient obtained
from the topographical map, groundwater level and permeability from borehole data, geology and land use. These factors were
employed by with artificial neural networks to analyze ground subsidence hazard. Each factor’s weight was determined by the
back-propagation training method. Then the ground subsidence hazard indices were calculated using the trained back-propagation
weights, and the ground subsidence hazard map was created by GIS. Ground subsidence locations were used to verify results
of the ground subsidence hazard map and the verification results showed 96.06% accuracy. The verification results exhibited
sufficient agreement between the presumptive hazard map and the existing data on ground subsidence area.
An erratum to this article can be found at |
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| Keywords: | Ground subsidence Abandoned underground coal mine Artificial neural networks GIS Verification |
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