# Application of Artificial Neural Networks in Assessing Mining Subsidence Risk
## 3 Information on/about Abandoned Mines and Datasets  
The subsidence data for this study were collected from 467 locations (one location means one subsidence point), among which subsidence had occurred at 388 locations, and there were 79 locations where subsidence had not occurred (Table 1). However, more than a few data points did not have any information on the goafs--e.g., the depth from ground to goaf, goaf height/width, and rock mass quality (RMR)--which has an essential impact on subsidence, or the information was very unclear. Such data were excluded from the analyses. Eventually, 227 locations from 37 abandoned mines were selected for the study. Among these, 118 locations were from 15 abandoned coal mines and 109 locations were from other types of mines (i.e., gold, silver, metal). The numbers of locations that did and did not display subsidence were 166 and 61, respectively. The locations of the selected abandoned mines are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Total (37)**} & \multicolumn{3}{c}{**At Coal Mines (15)**} & \multicolumn{3}{c}{**At Other Mines (22)**} \\ \cline{2-10}  & \multirow{2}{*}{**Sum**} & \multicolumn{3}{c}{**Subsidence**} & \multirow{2}{*}{**Sum**} & \multicolumn{3}{c}{**Subsidence**} & \multirow{2}{*}{**Sum**} & \multicolumn{3}{c}{**Subsidence**} \\ \cline{3-3} \cline{5-10}  & & **Yes** & & **No** & & **Yes** & **No** & & **Yes** & **No** \\ \hline Total collected data & 467 & 388 & 79 & 300 & 257 & 43 & 167 & 131 & 36 \\ \hline \multirow{3}{*}{Selected data} & Sum & 227 & 166 & 61 & 118 & 92 & 26 & 109 & 74 & 3  
Furthermore, 207 of the total 227 locations were used for machine learning, and the other 20 were used for validation of the prediction model during the machine learning process. The information on the mines and subsidence locations was collected from various mine reports published by the Mine Reclamation Corporation in Korea. The reports present the results of extensive investigations on subsidence conducted at various abandoned mines across the country. Meanwhile, many previous studies have identified important factors that contribute to ground subsidence around mines, including the depth and height of the mined cavities, excavation method, degree of inclination of the excavation, scope of mining, structural geology, and flow of groundwater [40, 41]. [PERSON] et al. [3] found that gradual deterioration due to weathering plays a significant role in the propagation of the cavity and the formation of sinkholes above abandoned mine workings. [PERSON] and [PERSON] [4] have analyzed the causes of 4 km long land subsidence around the mine causing damage to villages of Greece using the PLAXIS three-dimensional finite element code, and concluded that the factors which affect land subsidence (from the most dangerous to the least dangerous) are the groundwater drawdown, the geotechnical behavior of the formation, the tectonic structure, and the offset of the faults. [PERSON] and [PERSON] [2] mentioned that subsidence is a time-dependent process and controlled by various factors including methods of mining, depth of extraction, thickness of deposit, and topography, as well as the in-situ properties of the rock mass. [PERSON] et al. [9] concluded that factors of the relationship between surface dynamic subsidence and overburden separated strata include the requirement of abscission layer development (stress, stiffness, mining condition, and deflection) and surface subsidence conditions (mining thickness, mining depth, mining overburden rock properties, speed). [PERSON] et al. [10] developed a new time function model to predict the dynamic subsidence that occurs in the grout-injected overburden of a coal mine during overburden grout injection mining. This function includes the mining parameters (the width of the working face and the advancement length of the filled area), a geological parameter (the angle of full subsidence), grout injection parameters, and other relevant coefficients. [PERSON] et al.