## 2 Overview of the Study Area and Data Validity
### Data Validation (Assessing the Validity and Reliability of Data)  
In this study, the data used were collected from geotechnical investigation reports and final design reports [34; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47]. These data were comprised of the findings of geotechnical  
Figure 3: Zone classification of the Saemangeum reclaimed land.  
Figure 2: Geological map of the Saemangeum reclaimed land [34].  
investigations undertaken for the construction of the east-west road, north-south road, industrial city, and waterfront city in Saemangeum. Considering the variables utilized in empirical equations and machine learning models for predicting the compression index in previous studies, along with various factors influencing the compression index, four geotechnical parameters--natural water content (w\({}_{n}\)), liquid limit (LL), plasticity index (PI) and initial void ratio (e\({}_{0}\))--were selected as predictor variables for estimating the compression index (C\({}_{c}\)). The validity of these data was evaluated and explained by calculating the Pearson correlation coefficient. The Pearson correlation coefficient is a metric used to describe the linear relationship between variables.  
The calculated correlation coefficients between the compression index and the four predictor variables for the given dataset are presented in Table 1. All four variables demonstrated a relatively strong correlation with the compression index [48]. Furthermore, the computed \(p\)-values were all below 0.05, confirming their statistical significance as predictors for estimating the compression index.  
The geotechnical parameters used in this study were collected from borehole data spanning the entire Saemangeum area. After excluding missing values, a total of 479 datasets were compiled. These datasets were then further divided into zones 1, 2, 3, and 4 based on the hydrogeological characteristics, resulting in 272, 39, 33, and 135 datasets for each zone, respectively. Because the datasets for zone 2 and zone 3 were limited in size, there was a possibility that this constraint may have affected the accuracy of the training and testing outcomes.  
Subsequently, statistical analysis was conducted to accurately understand the distribution and range of the collected data (Table 2). The natural water content exhibited a relatively wide distribution, ranging from 17.7% to 68.3%, while the liquid limit demonstrated substantial variation between 24.9% and 94%. The plasticity index, with a standard deviation of 13.295, exhibited a distribution pattern similar to that of the liquid limit. The initial void ratio ranged from 0.541 to 1.88, indicating low variability, while the compression index, varying between 0.03 and 0.93, exhibited the smallest standard deviation among all geotechnical parameters.  
All analyses in this study were performed using Python version 3.11.5, along with the Scikit-learn library. The dataset was split into a training dataset for model training and a testing dataset for performance evaluation, with a ratio of 8:2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Factor** & **Correlation Coefficient** & \(p\)**-Value** \\ \hline
1 & w\({}_{n}\) & 0.7 & 0.000 \\
2 & LL & 0.7 & 0.000 \\
3 & PI & 0.69 & 0.000 \\
4 & e\({}_{0}\) & 0.75 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pearson correlation coefficients between the C\({}_{c}\) and soil properties.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Min** & **Max** & **Mean** & **Std** \\ \hline \multirow{3}{*}{Input Variable} & w\({}_{n}\) (\%) & 17.7 & 68.3 & 36.379 & 6.96 \\ \cline{2-6}  & LL (\%) & 24.9 & 94 & 46.482 & 13.046 \\ \cline{2-6}  & PI (\%) & 2.2 & 62.8 & 24.778 & 13.295 \\ \cline{2-6}  & e\({}_{0}\) & 0.541 & 1.88 & 1.019 & 0.181 \\ \hline Output Variable & C\({}_{c}\) & 0.03 & 0.93 & 0.359 & 0.127 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of the entire dataset.