# Application of Artificial Neural Networks in Assessing Mining Subsidence Risk
## 3 Information on/about Abandoned Mines and Datasets
[1] mentioned that factors related to subsidence are mining and geological conditions, mining methods, working face advance speeds, and mining period, amongst others. Figure 5: Locations of selected and investigated abandoned mines. Based on the literature study regarding the factors affecting subsidence, the following 11 potential influence factors for subsidence were identified: 1 elapsed time in years from the closing of the mine until an investigation was performed; 2 RMR; 3 groundwater level; 4 existence of an outcrop at the investigated location; 5 types of loads near the investigated location (bridge, railway, house, etc. ); 6 depth from ground to the nearest goal; 7 goaf height; 8 horizontal distance from the investigated location to the nearest goal; 9 uniaxial compressive strength (UCS) of the rock mass; 9 rainfall of the local area; and 10 occurrence or lack of occurrence of subsidence. As the second step of data pre-processing, correlation analyses between subsidence occurrence and the potential influential factors were conducted and the multicollinearity among the factors was assessed. Multicollinearity is when two or more of the independent variables being fed into the model are highly correlated. In regression models, this may cause regression coefficients to change erratically in response to small changes in the model or the data because of confounding between the influences of highly correlated factors. Thus, if the highly correlated input variables are identified and eliminated in advance, then the network can determine the most appropriate weights more efficiently [42]. The results showed that the correlation between subsidence occurrence and UCS, or rainfall was very low and that the multicollinearity between RMR and UCS, as well as between groundwater level and rainfall, was high (Figures 6 and 7). These mean that the effects of UCS and rainfall on subsidence are comparatively low, and RMR and groundwater level are closely related to UCS and rainfall respectively. Finally, UCS and rainfall were excluded from the factors for the analyses, and the other nine influence factors were selected, as depicted in Figure 8.  
Figure 6: Correlation between influence factors and subsidence. Figure 7: Correlation matrix for checking multicollinearity. Figure 9 presents the value ranges of the selected data for each factor. The time elapsed from the closing of the mine until an investigation was performed ranged between 7 and 23 years, with an average of 10 years, whereas the time elapsed for the other types of mines ranged between 0 and 71 years, with an average of 25 years. The average depth from ground level to the nearest goat at a subsidence location was 36 m for coal mines and 41 m for other types of mines. Furthermore, the average horizontal distance was approximately 7 m and 5 m, respectively. The number of investigated locations near a railway or road was 15 and 14, corresponding to about 12% and 13% of the total investigated locations (118 and 108 locations), respectively. The number of locations located on an outcrop was 20 and 25, which corresponds to about 24% and 18% of the total investigated locations, respectively. Figure 8: Selected influence factors. Figure 9: Ranges of investigated data for each factor.