###### Abstract  
As ground subsidence accidents in urban areas that occur due to damage to underground utilities can cause great damage, it is necessary to predict and prepare for such accidents in order to minimize such damage. It has been reported that the main cause of ground subsidence in urban areas is cavities in the ground formed by damage to underground utilities. Thus, in this study, attribute information and historical ground subsidence information of six types of underground utility lines (water supply, sewage, power, gas, heating, and communication) were collected to develop a ground subsidence risk prediction model based on machine learning. To predict the risk of ground subsidence in the target area, it was divided into a grid with a square size of 500 m \(\times\) 500 m, and attribute information of underground utility lines and historical information of ground subsidence included in the grid were extracted. Six types of underground utility lines were merged into single-type attribute information, and the risk of ground subsidence was categorized into three levels using the number of ground subsidence occurrences to develop a dataset. In addition, 12 datasets, which were developed based on the conditions of certain divided ranges of attribute information and risk levels, and 12 additional datasets, which were developed using the Synthetic Minority Oversampling Technique to resolve the imbalance of data, were built. Then, factors that represented significant correlations between input and output data were singled out and were then applied to the RandomForest, XGBoost, and LightGBM algorithms to select a model that produced the best performance. By classifying the ground subsidence risk levels through the selected model, it was found that density was the most important influencing factor used in the model. A risk map of ground subsidence in the target area was made through the model; the map showed the trend of well-predicted risk levels in the area where ground subsidence was concentrated. ground subsidence; machine learning; ground subsidence risk prediction model; risk map 20231022023102  
Extensive research has been performed on ways to prevent accidents related to ground subsidence. In Japan, a study using indoor model experiments simulating ground subsidence using standard sand was published to identify the mechanism of how cavities, a precursor to ground subsidence, were generated inside the ground, while a study on the identification of a cavity generation mechanism inside the ground by simulating a crack in the sewage pipeline under the soil box and visualization of the cavity generation through equipment such as X-rays and computed tomography has also been published [4; 5]. Research that aims to identify the mechanism of ground subsidence occurrence using numerical analysis has also been active. Using the finite element method to simulate the ground cavity and relaxation zone, several published studies have shown that the location of the underground utility damage, the relative density of the ground, and the ground layer conditions have a significant effect on the ground subsidence [6; 7; 8]. In addition, studies on performing a decision tree, which is one of the machine learning algorithms, and the analytic hierarchy process, were published to derive factors influencing ground subsidence and calculate the weights of influencing factors [9; 10]. Studies aiming to predict the risk level of ground subsidence have also been steadily conducted. One study on the evaluation of ground subsidence risk level that was announced uses surveyed CCTV data based on sewage pipelines, which is the main cause of ground subsidence, as well as cavity exploration data by underground exploration radar. In addition, a study was conducted to propose a regression equation of the ground subsidence risk level in urban areas in Korea through logistic regression analysis [11].