# Application of Artificial Neural Networks in Assessing Mining Subsidence Risk
## 6 Conclusions  
This paper establishes a mine subsidence risk index and a subsidence risk grade based on two separate analyses using ANN to predict whether subsidence can occur at a ground location of an abandoned mine. The primary conclusions of the study are summarized below:  
(1) From a total of 467 investigated locations in this study, 227 locations from 37 abandoned mines were selected for analysis. 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 where subsidence had and had not occurred were 166 and 61, respectively. (2) The correlation and multicollinearity analyses among 10 factors related to mine subsidence were performed to improve the prediction accuracy. 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. This means 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, eight factors excluding UCS and rainfall (i.e., elapsed time, depth to goat, goat height, distance to the nearest goat, RMR (rock mass rating), groundwater level, load types on the investigated location, and the existence of an outcrop on the investigated location) were selected. (3) The analysis consisted of two separate stages, analysis A and analysis B. Analysis A was conducted to predict whether subsidence could occur using an ANN model of machine learning. Analysis B involved assessing the mine subsidence risk index to indicate the extent of risk for subsidence at the location. After training the model in analysis A, the results showed that the correct prediction percentages for the validation datasets of coal mines and other types of mines were 83% and 75%, respectively. In analysis B, the eight factors were divided into two groups, a direct influence factor group (depth to a goat, goat height, and horizontal distance to the nearest goat) and an indirect influence factor group (elapsed time, RMR, load type, outcrop, and groundwater level), to assess the mine subsidence risk (MSR) index. The final determination of subsidence possibility is made using a subsidence risk grade that is established based on the two analyses. The analysis results showed that the correct prediction percentage was 90% (=9/10 subsidence locations) for the 10 validation data of coal mines and 100% (=7/7 subsidence locations) for the 7 validation data of other types of mines. (4) To verify the prediction model, new datasets for 22 locations--including 9 locations with no subsidence--were selected from 5 abandoned mines and investigated. The correct prediction percentage for coal mines was 83% (=5/6 subsidence locations), and the correct prediction percentage for other types of mines was 86% (=6/7 subsidence locations). Although this model performed reasonably well in the assessment of potential subsidence at various locations, the sample size was relatively small; thus, additional analyses should be performed at other mine locations and in a larger variety of ground conditions to more clearly verify the model. (5) It is thought that more reliable results can be obtained when various types of analyses by numerical, statistical, and theoretical approaches are combined. This study can be used as an initial method for screening the specific locations that are at high risk of subsidence and thus require immediate additional investigation, from an extensive mine area for subsequent detailed analyses by the other approaches. Conceptualization, Y.K. ; Validation, S.S.L. ; Formal analysis, Y.K. ; Data curation, Y.K. ; Writing--original draft preparation, Y.K. ; Writing--review, S.S.L. ; Supervision, S.S.L. ; Project administration, S.S.L. ; Funding acquisition, Y.K. All authors have read and agreed to the published version of the manuscript.