## 1 Introduction  
As large-scale infrastructure is built along coastal areas with soft ground, concerns are emerging about damage to superstructures due to the consolidation settlement of soft soil. Soft ground refers to very soft ground composed of layers of silt and clay. Soft soils have different geotechnical characteristics depending on certain conditions, such as how they were created, their sedimentary environment, and mineral composition [1]. Accordingly, the compression index of each region has different characteristics and ranges, and those considering ground characteristics are required to estimate consolidation settlement accurately. However, there are difficulties in conducting precise geotechnical surveys in wide areas due to the time and costs involved. As a result, the amount of soft ground settlement is generally predicted by estimating the overall soil properties based on geotechnical surveys.  
The compression index of soft ground can be derived from consolidation tests in the laboratory using samples collected in the field [2]. The compression index is often used to calculate the primary consolidation settlement for consolidated or overconsolidatedground [3]. The compression index and primary consolidation settlement show a proportional relationship [4]. The compression index is calculated from the void ratio and the compression curve's slope of the effective overburden pressure (effective stress) obtained from a standard consolidation test [5]. However, standard consolidation tests are expensive and time-consuming. Therefore, many empirical formulas have been developed to calculate the compression index from relatively simple physical test results [6; 7; 8; 9; 10]. Many studies have been conducted to predict the compression index by applying various techniques, such as developing a model for predicting the compression index through single and multiple regression analysis using the natural water content, liquid limit, and initial void ratio [8]. In addition, many studies have also been conducted on developing compression index prediction models based on machine learning algorithms [11; 12; 13]. However, most previous studies developed these models using relatively little data derived from local areas. In addition, different kinds of soft ground have different characteristics depending on various factors (such as how they were formed), so there are limitations to applying compression index prediction models from previous studies to other regions. Therefore, this study analyzed the characteristics of geotechnical data of the soft ground from Korea's south coast and proposed a compression index prediction model using linear regression analysis and machine learning techniques. The geotechnical data of the target area (natural water content, liquid limit, plastic limit, plasticity index, initial void ratio, compression index) were collected and analyzed. Then, Pearson correlation analysis was used to determine the compression index and significant geotechnical properties, and regression equations between the geotechnical properties and compression index were derived through linear regression analysis. After removing any missing values, compression index prediction models were developed by applying the dataset to machine learning algorithms, such as RandomForest (RF), XGBoost (XGB), and LightGBM (LGBM). After this was completed, the significance of the factors used by the model to predict the compression index was investigated. Using the compression index prediction model proposed in this study will enable the prediction of the approximate compression index of the target area, and securing more data will enable the development of a model with upgraded quality in the future.