# Prediction of Ground Subsidence Risk in Urban Centers Using Underground Characteristics Information
## 1 Introduction  
Ground subsidence refers to the collapse of the surface resulting from the formation and enlargement of cavities due to the loss of soil particles beneath the ground [1]. Ground subsidence is most prevalent in actively developed urban areas. Damage to underground infrastructure and adjacent excavation activities are the primary causes of ground subsidence [2]. Ground subsidence harms individuals and property and thus is a significant social concern. Therefore, it is essential to identify the causes of ground subsidence and implement effective strategies for detecting ground subsidence and mitigating damage.  
The primary cause of ground subsidence has been identified as damage to water supply and sewer pipes. Damage to water supply and sewer pipes leads to groundwater infiltration. During this process, the adjacent soil particles erode, resulting in the formation of cavities in the ground. It illustrates a mechanism in which the joints progressively enlarge and are unable to support the imposed load, resulting in a collapse [1]. Ground subsidence occurs through a mechanism in which the upper ground collapses due to the formation and expansion of voids within the subsurface. Multiple factors apart from underground infrastructure can lead to the formation of cavities. Therefore, the different factors that affect the formation of cavities must be investigated to proactively prevent ground subsidence [3].  
Numerous studies have been conducted to examine the mechanisms of ground subsidence and identify the influencing factors. Indoor model experiments simulating ground subsidence were conducted using standard sand in a model soil box in Japan and South Korea to identify the mechanism of cavity formation in the ground [4,5]. In a previous study,the mechanism of cavity formation due to groundwater inflow and outflow was visualized and verified by simulating pipe damage beneath the model soil box [6]. Numerical analysis techniques have been used to simulate ground subsidence and examine the influence of ground conditions on the occurrence of subsidence [7; 8]. Correlation analysis and AHP (Analytic Hierarchy Process) analysis have also been used to identify the factors influencing ground subsidence.  
Statistical methods and artificial intelligence techniques have also been used to predict ground subsidence risk based on influencing factors. The machine learning algorithm decision tree has been used to analyze the risk of ground subsidence [9]. Logistic regression analysis and spatial regression analysis have been used to predict the risk of ground subsidence [10]. Methods have been proposed to predict the risk of ground subsidence by analyzing the density of underground pipelines [11; 12]. Machine learning and deep learning techniques have been employed for predicting ground subsidence risk and generating risk maps that are based exclusively on the attribute information of underground facilities [13].  
Ground subsidence results from a complex interplay of numerous factors. Therefore, data pertaining to other aspects of the underground space, along with the underground facility attribute information, must be exploited to improve the reliability of ground subsidence risk predictions. In this study, we developed a machine learning-based model to predict the ground subsidence risk in urban centers in South Korea. Previous studies have considered data on existing ground subsidence history and underground facility attributes. In addition to the data used in previous studies, we consider the data on permeability coefficient, stratigraphic thickness, and height [13]. We also visualize the risk map for the target areas. The data classification method presented in this study leads to optimal performance owing to the use of various datasets to train the machine learning algorithms. The results of this study highlight the importance of the influencing factors used in the model for classifying the risk of ground subsidence.