## 2 Method and Data Characteristics
### Characteristics of the Data  
A representative area in Korea was selected as the target region for the prediction of ground subsidence risk level. The target region was divided into a grid with a total of 2391 squares of 500 m \(\times\) 500 m in size for the risk level evaluation. For each grid square, the attribute data of six types of underground utility lines (water supply, sewage, power, gas, heating, and communication cables) and the historical data of ground subsidence were compiled. As described above, six types of underground utility lines were merged into a single type to extract attribute data. In the attribute information of underground utility lines to build a dataset, the number of years used, pipeline type, diameter, length, burial depth, slope, etc., were included, but there were many missing and erroneous values as well. Thus, as the data that could be usable, the number of years used, pipeline diameter, and length were selected. Then, the density of all pipelines was calculated to be used as a factor influencing the occurrence of ground subsidence. To improve the model's performance, raw data were not directly used, but they were preprocessed to divide the attribute information of underground utility lines by a certain range. The years used were divided into 5- and 10-year units, and the pipeline diameter was divided into 50 mm and 100 mm units. The basic unit of data that belongs to the corresponding range was set to the pipeline's length. For example, an underground pipeline that was used for three years in  
Figure 1: Flow chart. the grid was assigned to a class corresponding to an age of 1 to 4 years, and the length of the pipeline was reflected. For the output data, the risk level of ground subsidence was calculated by summing the number of ground subsidence occurrences in the grid using the historical information of ground subsidence occurrences. It is difficult to provide a quantifiable measure of ground subsidence risk. Thus, multiple datasets of ground subsidence risk levels, categorized by the number of ground subsidence occurrences, were developed. The developed datasets were applied to the machine learning algorithms to select a condition of the risk level of ground subsidence that exhibited good performance. The ground subsidence risk was categorized into three levels. Risk Level 1 means an area where the number of ground subsidence occurrences in the grid is \"0\". The conditions of Risk Levels 2 and 3 were adjusted depending on the number of ground subsidence occurrences in the grid. If the number of ground subsidence occurrences in the grid of Risk Level 2 is one, the number of ground subsidence occurrences of Risk Level 3 was set to two or more. If the number of ground subsidence occurrences in the grid of Risk Level 2 is set to a range of one to two, the number of ground subsidence occurrences of Risk Level 3 was set to three or more. If the number of ground subsidence occurrences in the grid of Risk Level 2 is set to a range of one to three, the number of ground subsidence occurrences of Risk Level 3 was set to four or more. Risk Level 1 of ground subsidence means a relatively safer area from ground subsidence. The boundary between Levels 2 and 3 varies depending on the conditions, but Level 2 means an area that needs attention, and Level 3 is an area that is at the highest risk. Table 1 presents the categories of factors in the datasets. Table 2 presents the dataset which is set according to the data category condition. A total of 24 datasets were built according to whether or not SMOTE was applied to each dataset.