## 1 Introduction
However, using the liquefaction risk as a categorical variable has the disadvantage that it cannot be treated as a quantitative indicator. From a practical perspective, this is considered inadequate for quantitative liquefaction analysis. In particular, when designing appropriate evacuation routes in the event of a major earthquake, specific values are required to estimate the points where evacuation routes are likely to be disrupted, as well as the delay in evacuation time. Usually, the amount of subsidence is determined by the standard penetration test N-values (SPT-N values), which indicate the ground strength, or soil physical parameters based on the factor of safety against liquefaction (FL) method [20]. Although this method can achieve high accuracy over a limited area, it relies on point data and is thus not suitable for wide-area estimation. This is because of the enormous amount of experimental data that is required to determine liquefaction over an extensive area, such as a city, town, or village. Studies have been conducted to determine liquefaction over extensive areas using geographic information systems (GIS); the method proposed by [PERSON] et al. [21] for estimating liquefaction risk can predict liquefaction over extensive areas based on geomorphological classifications. Additionally, [PERSON] et al. [22] developed an assessment model to predict the probability of liquefaction on a global scale using a global geospatial liquefaction model (GGLM). It is important to predict the areas in which liquefaction will occur in preparation for future large earthquakes, and it is desirable to be able to calculate liquefaction in a simple and highly accurate manner over an extensive area. Therefore, there is an increasing need for liquefaction risk assessment and countermeasures. The dominant factors that govern liquefaction include earthquake seismic intensity, duration of shaking, ground type, and groundwater level [23; 24]. However, it is difficult to express these as regression equations and adequately capture the interactions. In this context, advances in machine-learning technology have created new possibilities. Machine learning is a technique used to learn complex patterns from large amounts of data and to construct predictive models. Liquefaction has been assessed using machine learning, with studies evaluating liquefaction based on the physical parameters of the ground [25]. Recently, a novel semi-active control algorithm that combines a fuzzy control strategy and long short-term memory (LSTM) was proposed with the aim of mitigating structural responses under earthquake excitations [26]. [PERSON] and [PERSON] [27] applied the machine learning algorithms to estimate liquefaction susceptibility in the Metro Manila, Philippines. [PERSON] and [PERSON] [28] used random forest (RF), a machine learning algorithm, as an estimation model, and input earthquake scenario ground motion data to develop a Japanese liquefaction risk map. [PERSON] and [PERSON] [29] successfully applied explainable AI (XAI) to perform factor analysis based on seismic parameters, ground parameters, and site conditions. This suggests that factor analysis of liquefaction scenarios can elucidate the interactions and nonlinear relationships between multiple factors that have not been captured by previous statistical methods. However, these previous studies only developed predictive models for the presence or absence of liquefaction, and there are few examples of studies that have revealed the quantitative magnitude of liquefaction and the factors that influence the amount of ground subsidence. Quantitative predictive data are more important than qualitative data for estimating the degree of damage after liquefaction has occurred. In this study, a model for quantitatively predicting ground subsidence due to liquefaction was developed by integrating existing seismic data, topographical data, and geotechnical information, with the aim of improving resilience against liquefaction damage.