# An Overview of GIS-Based Assessment and Mapping of Mining-Induced Subsidence
## 5 Discussion
### Uncertainty of Subsidence Inventory Data and Time Effect  
Input data (i.e., subsidence inventory map and influencing factor map) must be accurate and reliable because they can have a significant influence on the subsidence prediction result. However, numerous GIS-based subsidence studies investigated in this paper have data uncertainty problems from two perspectives. The first is the diversity of mining subsidence types. In general, mine subsidence occurs in various forms such as continuous (trough) settlement, discontinuous (sinkhole) subsidence, cracks, curvature, and other phenomena. The cause and degree of damage for each type differ. Therefore, the diversity  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Mine Panel**} & \multirow{2}{*}{**SSS Score \({}^{\star}\)**} & \multicolumn{3}{c}{**Initial Yield**} & \multicolumn{3}{c}{**Subsided Yields**} & \multicolumn{3}{c}{**Mitigated Yields**} \\ \cline{3-10}  & & **Total (Mg)** & **Mg/ha** & **Total (Mg)** & **Mg/ha** & **\% Loss** & **Total (Mg)** & **Mg/ha** & **\% Loss** \\ \hline
1 & 9.9 & 312 & 6.43 & 297 & 6.12 & 4.8 & 309 & 6.38 & 0.7 \\ \hline
2 & 10.7 & 327 & 6.37 & 309 & 6.01 & 5.6 & 324 & 6.31 & 0.9 \\ \hline
3 & 8.7  
of the types of ground subsidence should be considered in the subsidence inventory data. However, most of the studies investigated so far only visualize the zoning information of ground subsidence. Therefore, it is necessary to classify the types of ground subsidence, analyze their cause, and predict the possibility of future occurrence. The second is the elapsed time effect (timing of the creation) of the underground mining cavities. According to published literature on the relationship between subsidence and time [1,50,51], the strength of the rock in the upper part of the underground cavity decreases owing to various causes, such as sagging caused by a loss of bearing capacity and changes in the groundwater level over time. In other words, even if it is safe, the probability of ground subsidence will increase over a long period of time (e.g., residual subsidence). Therefore, it is necessary to consider factors such as the elapsed time after the mine is abandoned in addition to the location data of the underground cavity in future studies. This part is also related to the development of 4D GIS that considers temporal aspects in 3D space. Lack of Interpretation of the Cause of the Prediction Accuracy Difference (Applicability of Analytic Methods)  
Most of the studies discussed in this review have presented the prediction accuracy for composed subsidence prediction maps based on specific analytical methods or approaches. In comparative studies [32, 33, 34, 35, 36, 37, 38], the prediction accuracies based on two or more methods applied to subsidence prediction mapping were compared, and some papers have compared their prediction accuracy with those of other studies to verify the superiority of the proposed analytical methods or approaches. However, it is difficult to find studies that faithfully present the interpretations or evidence for why method 'A' has a higher prediction accuracy than method 'B'. The use of method 'A' is often mentioned as more appropriate in the study area or in the subsidence prediction study because the prediction accuracy of method 'A' is higher. Alternatively, these studies may contain the interpretation that machine learning or deep learning techniques consider more factors than the general probability/statistical technique in which the characteristics of the contributory factors are not considered. To enhance GIS-based subsidence research, it is necessary to determine the correct answer to this research gap. The prediction accuracy can be varied according to the reliability of the subsidence occurrence data, cell size, characteristics of the study area, influential factors, and the characteristics of the applied technique. However, it is crucial to carefully investigate the characteristics and applicability of each analysis technique applied to subsidence data.