Assessment of ground subsidence hazard near an abandoned underground coal mine using GIS |
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| Authors: | Ki-Dong Kim Saro Lee Hyun-Joo Oh Jong-Kuk Choi Joong-Sun Won |
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| Institution: | (1) Department of Earth System Sciences, Yonsei University, 134 Shinchon-dong, Seodaemun-gu, Seoul, 120-749, Korea;(2) Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources (KIGAM), 30, Gajung-Dong, Yusung-Gu, Daejon, 305-350, Korea |
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| Abstract: | This study constructs a hazard map for ground subsidence around abandoned underground coal mines (AUCMs) at Samcheok City in Korea using a probability (frequency ratio) model, a statistical (logistic regression) model, and a Geographic Information System (GIS). To evaluate the factors related to ground subsidence, an image database was constructed from a topographical map, geological map, mining tunnel map, Global Positioning System (GPS) data, land use map, lineaments, digital elevation model (DEM) data, and borehole data. An attribute database was also constructed from field investigations and reports on the existing ground subsidence areas at the study site. Nine major factors causing ground subsidence were extracted from the probability analysis of the existing ground subsidence area: (1) depth of drift; (2) DEM and slope gradient; (3) groundwater level, permeability, and rock mass rating (RMR); (4) lineaments and geology; and (5) land use. The frequency ratio and logistic regression models were applied to determine each factor’s rating, and the ratings were overlain for ground subsidence hazard mapping. The ground subsidence hazard map was then verified and compared with existing subsidence areas. The verification results showed that the logistic regression model (accuracy of 95.01%) is better in prediction than the frequency ratio model (accuracy of 93.29%). The verification results showed sufficient agreement between the hazard map and the existing data on ground subsidence area. Analysis of ground subsidence with the frequency ratio and logistic regression models suggests that quantitative analysis of ground subsidence near AUCMs is possible. |
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| Keywords: | Ground subsidence Abandoned underground coal mine Frequency ratio Logistic regression GIS Korea |
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