## 4 Conclusions  
To develop a model that predicts the risk level of ground subsidence and create a risk level map targeting the urban area in South Korea, a dataset was built using the pipeline length, the number of years used, and the diameter and density of pipelines in the target area. The developed datasets were applied to machine learning algorithms RF, XGB, and LGBM, to comparatively analyze the evaluation indexes. Through this process, the best performance was found in the model with the following dataset conditions applied to the XGB classifier: the number of years used was five years, the pipeline diameter was 50 mm, and the number of ground subsidence occurrences in the grid with risk Level 2 of ground subsidence was set to 1 to 3, when using SMOTE applied data (F1 Score = 0.590, AUC = 0.8). Previously, a machine learning-based ground subsidence risk prediction model has been developed for a small subset of urban areas (two districts) in South Korea [15]. However, since the model was trained using data from a very small area, it is not reliable enough to be applied to a wide range of target areas. Thus, in this study, we collected a large number of data for the entire city and proposed a model for predicting the risk of ground subsidence. As a result, it is now possible to create a reliable ground subsidence risk map for urban areas in Korea through the ground subsidence risk prediction model presented in this study.  
Figure 4: Map of ground subsidence risk. (**a**) Prediction map of ground subsidence risk level. (**b**) Map of ground subsidence risk using real data.  
The ground subsidence risk prediction model presented in this study derives the importance of influencing factors used when classifying the risk level of ground subsidence. Our study results verified that the density had the highest importance, and the number of years used was more important than the pipeline diameter. This result is similar to that of a previous study which found the density and the number of ground subsidence occurrences were highly correlated [9], as well as another study where the aging of pipelines had an impact on the ground subsidence occurrence as the number of years used increased [3]. Thus, excavation work to bury underground utility lines should be minimized, and aged pipelines should be managed to cope with ground subsidence.  
The risk level map of ground subsidence in the target area was created using the ground subsidence risk prediction model. This map predicted a number of spots with higher risk levels than that in the risk map based on the historical data of past ground subsidence. The ground subsidence risk prediction classifier presented in this study predicted the risk level of the area in which ground subsidence was concentrated in the past relatively well.  
It is expected that the results presented in this study can be used as foundational data for a proactive response to the occurrence of ground subsidence in urban areas. In future research, we will add underground structures (subway tunnels, etc.) and high-rise building information in the target region to develop a more reliable prediction model of ground subsidence risk level in urban areas.  
Conceptualization, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); developed the models and carried out the model simulations, S.L.; writing--original draft preparation, S.L.; writing--review and editing, [PERSON] ([PERSON]) and [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant from the project \"Underground Utilities Diagnosis and Assessment Technology (4/4),\" which was funded by the Korea Institute of Civil Engineering and Building Technology (KICT).  
Not applicable.  
The authors declare no conflict of interest.