# 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
#### 3.2.3 Machine Learning and Deep Learning  
Because of the recent growing interest in data mining technology worldwide, the use of machine learning methods for evaluating mining subsidence has become widespread in the last few years. However, although various deep learning techniques have recently been proposed, only limited techniques have been used in the field of mining subsidence.  
An artificial neural network (ANN) method was utilized to predict ground subsidence in a GIS environment. [PERSON] et al. [28] attempted to generate subsidence susceptibility maps using the ANN method with seven factor maps in a GIS environment. The weight of each factor was calculated using the back-propagation training method. Three-layered feed-forward networks (\(7\times 15\times 1\) structure for each) were created using the MATLAB software package in this study. A subsidence susceptibility map was compiled using the determined weights of each factor. The verification results showed a 96.06% accuracy and exhibited sufficient agreement between the presumptive hazard map and the existing data on ground subsidence area. Similarly, [PERSON] et al. [29] spatially predicted mining subsidence susceptibility using the ANN technique and eight contributory factors. The analysis procedures and methods of this study were similar to those of the previous study, except for the structure of the network (\(8\times 16\times 1\)). The results of the relative importance and weights of the factors revealed that the distance from the fault showed the highest value of 1.5477, followed by the geology factor in this case.  
Figure 4: An exemplary CFD–AUC verification technique for calculating the prediction accuracy of the two subsidence prediction maps.  
[PERSON] et al. [30] mapped ground subsidence hazards using an adaptive neuro-fuzzy inference system (ANFIS) model with different types of FMFs. The ANFIS method adopts neural network learning algorithms and fuzzy reasoning to map inputs into an output. This is similar to the fuzzy inference system in the framework of adaptive neural networks. The validation results showed similar prediction accuracy: 95.12% for the generalized bell-shaped MF model and 94.94% for the Sigmoidal2 MF model, which suggested that the choice of MFs was not important in the study. The authors of this paper concluded that the ANFIS technique showed an excellent predictive capability in subsidence prediction by combining the expert system (fuzzy inference) with the learning ability of the ANN method.  
[PERSON] and [PERSON] [31] constructed decision trees (DTs) using the chi-squared automatic interaction detector (CHAID) and quick, unbiased, and efficient statistical tree (QUEST) algorithms to analyze the relationships between past subsidence and related factors, as well as to map subsidence susceptibility near abandoned underground coal mines. Both subsidence susceptibility maps achieved by the DT model showed a better prediction accuracy (94.01% for the CHAID algorithm and 90.37% for the QUEST algorithm) than the FR model (86.70%).  
[PERSON] et al. [36] adopted four machine learning algorithms for land subsidence stability in mining areas. The four models include Bayesian logistic regression (BLR), support vector machine (SVM), logistic model tree (LMT), and alternate decision tree (ADTree). The seven subsidence-influencing factors were selected using the least square SVM technique. As a result, the most important subsidence conditioning factor for the study area was the distance to the lineament factor. This was followed by land use, lithology, lineament density, RMR, slope angle, distance to drift, and drift density. The validation results showed that the BLR model was the most distinguished model for subsidence management.