## 1 Introduction  
The number of ground subsidence occurrences has dramatically increased in the city Seoul, Republic of Korea, since 2010, as illustrated in Figure 1[1].  
This phenomenon can be extremely detrimental for various infrastructural and residential reasons. Ground subsidence herein needs to be defined differently from \"sinkhole\", which is common in limestone kars, and is associated with sub-circular surface depressions or collapsing structures  
Figure 1: (**a**) A snapshot of ground subsidence in Seoul and (**b**) number of ground subsidence that occurred in Seoul between 2010 and 2014.  
formed by the collapse of small subterranean karst cavities [2; 3]. Consequently, a sinkhole is deemed more likely a natural phenomenon than a ground subsidence. In accordance with U.S. Geological Survey (USGS) [4], a ground subsidence is a gradual setting or sudden sinking of the ground surface due to subsurface movement of earth materials.  
Extensive field investigations have been conducted by the Seoul Metropolitan Government to identify the causes of ground subsidence. The findings of this investigation can be summarized as follows [1]:  
* Since 2010, around 600 ground subsidences have mainly taken place annually in roadways, sidewalks, and vicinities of underground construction areas.
* Around 40 percent of events occurred during the summer season (June to August), which has frequent heavy rainfalls ranging from 50-100 mm/h.
* The causes of the events have been categorized as: (1) Damage to water and sewage utilities, which allows surrounding soil loss through the holes made; (2) Inappropriate backfill compaction during excavation activities that include open-cut construction and installation of underground utilities; (3) Drop in the ground water table due to pumping activities or damage to sheet pile wall.  
Several previous studies have established hazard models with respect to sinkholes, incorporating various data interpretation techniques [2; 5; 6]. These models mainly depend on the frequency of event occurrences of sinkholes, since they are mostly associated with a specific geological condition which can be relatively clearly identified. Since the causes of ground subsidence in this study are associated with the deterioration of buried utilities and improper construction activities, it is crucial to establish a relevant database to develop a risk model. With a given robust database, various methodologies can be adapted to develop ground subsidence susceptibility and hazard models. There are previous studies that deal with ground subsidence risk assessment due to underground mining and installation of underground box structures based on field measurements and numerical analysis [7; 8].  
Artificial Neural Network (ANN) is a complex mathematical model, or computational model, that is inspired by the structural aspects of biological neuron networks, which are widely used in the modelling of nonlinear systems and system identification [8]. [PERSON] [9] conducted an extensive review study on the application of ANN in geotechnical engineering. According to this reference, ANN has been drastically employed since the early 1990s, such as constitute modelling, geo-material characterization, assessment of bearing capacity of pile, slope stability, evaluation of liquefaction, shallow foundations, and tunnels and underground openings. In this study, two ANN-based models, support vector machine (SVM) [10] and multi-layer perceptron (MLP) [11], were employed to establish the risk model with respect to the underground utilities (mainly water supply and sewer pipe systems) along the railway network.