Ground Subsidence Hazard Analysis in an Abandoned Underground Coal Mine Area using Probabisltic and Logistic Regression Models

For quantitative analysis of presumptive ground subsidence near Abandoned Underground Coal Mine (AUCM), this study applied, verified and compared a probability model, a frequency ratio and statistical model, a logistic regression at Simpori of Samcheok city in Korea, using a Geographic Information System (GIS). To evaluate susceptibility of the subsidence, database was constructed using topographical map, geological map, mining tunnel map, GPS data, land use map, lineaments, DEM and borehole data. Then, the relationship between the factors and the existing subsidence were calculated using frequency ratio and logistic regression models. The relationships, frequency ratio and logistic regression coefficient, were overlaid to determine each factor's rating for ground subsidence susceptibility mapping. Then the ground subsidence susceptibility map was compared with known ground subsidence locations and verified. The verification results showed very high prediction accuracy (about 90%). The frequency ratio model (93.29%) had higher prediction accuracy than logistic regression (89.35%). The maps can be used to reduce hazards associated with ground subsidence and to land cover planning.