# An Overview of GIS-Based Assessment and Mapping of Mining-Induced Subsidence
## 3 Mine Subsidence Susceptibility/Hazard Assessment and Mapping
### General Procedure  
In general, subsidence susceptibility mapping research comprises the following four stages: data processing, data analysis, data mapping, and validation (Figure 2). Many GIS techniques, such as calculation, conversion, interpolation, map algebra, and other functions, are utilized in all these stages.  
The first step is to collect the GIS data from a study area. A variety of geospatial data are incorporated into the GIS database, such as subsidence inventory maps (zoning or deformation), mine cavity (drift or panel) maps, topographic maps, hydrology maps, land use maps, road maps, building maps, geology maps, borehole data, and other site investigation data. Various subsidence contributory factor variables are selected from such GIS databases and processed as grid cell raster type thematic layers using GIS-based spatial analysis and interpolation techniques. In the process of compiling thematic maps, the size of the grid cell (spatial resolution) must be set to a range of 1 m to 10 m considering the extent and size of the study area. Numerous studies have adopted six to eight subsidence contributing factors. Subsequently, all the collected data are divided into training data and validation data to perform an unbiased analysis. In general, the ratio of the training area to the validation area is set to 70%:30%; however, the data split work does not require a theoretical approach.  
The second step is to derive and interpret the correlation between past subsidence occurrences and the considered factors by analyzing and comparing the subsidence inventory map and each factor map (or all factor maps) spatially based on the analytical method or model. The analytical method or model includes probabilistic or statistical methods, fuzzy theory, expert systems, machine learning or deep learning, and an integrated approach. Correlation analysis is frequently performed outside the GIS environment, such as with the help of statistical analysis solution software or data mining software. However, when employing a theoretical approach in predicting future subsidence susceptibility, correlation analysis is not required.  
The third step involves determining the subsidence susceptibility index (SSI) across the entire grid cell of the raster layer and generating a subsidence susceptibility map representing the relative possibility of subsidence occurrence in the study area in a GIS environment. Areas with high SSI values (in general, expressed as red shading) show high subsidence potential; therefore, the SSI value can be  
Figure 2: Typical flow chart for assessing mine susceptibility within a GIS environment.  
used as a criterion to rank an area in terms of its subsidence hazard. Occasionally, the susceptibility map is also visualized along with past subsidence zones and nearby elements at risk, such as buildings or infrastructure.  
The fourth step involves assessing the predictive capability (prediction accuracy) of the generated subsidence susceptibility map. The SSI enables the ranking of areas in terms of the likelihood of subsidence occurrence. To verify the accuracy of the SSI predictions, the generated subsidence susceptibility map is compared with a subsidence inventory map showing the distributions of actual past subsidence occurrences. The most common approach to the verification of prediction maps in subsidence contexts is to construct a cumulative frequency diagram (CFD) or a success rate curve (SRC) by comparing the generated map with the locations of past subsidence occurrences and subsequently calculating the area under the curve (AUC). This technique derives a prediction accuracy in the range of 50-100% of the proposed prediction model. The yielded prediction accuracy demonstrates the efficacy of the generated map in subsidence susceptibility predictions.