# An Overview of GIS-Based Assessment and Mapping of Mining-Induced Subsidence
## 3 Mine Subsidence Susceptibility/Hazard Assessment and Mapping
### Literature Review According to Analytic Methods and Approaches
#### 3.2.1 Probabilistic/Statistical Approach  
Probabilistic/statistical methods are utilized to predict the possibility of future subsidence by analyzing spatial data from an inventory of past subsidence events; this was done with the assumption that subsidence occurrences are determined by specific subsidence-related factors, and that future subsidence events will occur under conditions similar to those of past subsidence events. Some studies have adopted the weight of evidence (WoE) method, which is a Bayesian probability model, to assess the susceptibility of mining subsidence. This method calculates the weight for each predictive factor based on the presence or absence of the training point subsidence units within the area of each binary predictor theme. [PERSON] and [PERSON] [23] assessed ground subsidence susceptibility at abandoned coal mine sites in Samcheok city in Korea using GIS and the WoE model. In this study, seven parameters were considered as subsidence-influencing factors, such as the depth of drift (cavity), distance from drift (cavity), geology, land use, slope, depth of ground water, and permeability. The spatial resolution (grid cell size) of the subsidence inventory map and contributory map was set to 1 m \(\times\) 1 m. The WoE model was utilized to derive the correlation between the past subsidence binary value and each contributory factor value. The predicted map showed a 96.67% prediction accuracy based on the area using the ROC validation technique. [PERSON] et al. [24] evaluated mining subsidence susceptibility using the WoE model by considering 3D complexed multiple mine drifts and estimated mined panels rather than employing 2D-based factors such as drift depth and distance to the mine drift to incorporate the complex effects of ground instability in the influential area. The SSI was calculated through the application of the WoE model, and it was shown on a map along with the damage level of buildings located in the mine area (Figure 3). Validation using the CFD-AUC technique revealed that the suggested approach showed a 5.51% higher prediction accuracy than the case using 2D-based factors. The FR model has also been used to evaluate subsidence susceptibility. The FR is the ratio of the area where an event (in this case, subsidence) occurred in the total study area and is the ratio of the probabilities of an event occurrence to a nonoccurrence for a given attribute. The application process of the FR model for subsidence prediction is similar to that of the WoE method, and the calculation procedure is relatively simple. [PERSON] et al. (2015) predicted areas vulnerable to mining subsidence near abandoned underground coal mines. This study applied the FR model to determine each subsidence-influencing factor's relative rating (for each class of the factor), and then the coefficient of determination (R\({}^{2}\)) between past subsidence occurrence and each factor was derived. Unfortunately, although there were many cases in which the coefficient of determination, which indicates the degree of relevance between ground subsidence and a specific factor, was less than 0.5 (low correlation), these factors were considered in the assessment of mine subsidence susceptibility. [PERSON] et al. (2016) analyzed mining subsidence susceptibility by combining the FR model and radius of influence concept, which averages the FR value of each grid cell as well as surrounding cells within the radius of influence specified from the drift depth and the break angle. The resulting enhancement in prediction accuracy proved that this technique enabled the reinforcement of preceding statistical approaches by suggesting method averages. The verification result using the CFD-AUC technique (Figure 4) revealed that the prediction accuracy of the suggested method (75.90%) was 8.31% higher than that of the existing method (the radius of influence concept was not considered) (67.49%).