## 4 Model Prediction Results and Analysis
### Impact of Clay Minerals  
When bedrock in a region undergoes weathering, primary minerals like feldspar and mica are produced. With further weathering, secondary minerals, including clay minerals, are generated. The type and amount of clay minerals within the soil have a significant impact on its expansiveness. Therefore, the impact of the clay mineral content and type on the performance of ML models was analyzed. As shown in Table 5, zone 4, influenced by the Dongjin River, exhibited a higher average R\({}^{2}\) compared with zone 1, which is affected by the Mangyeong River. This difference can be attributed to variations in the bedrock underlying the two rivers. Zone 1 is influenced by the Mangyeong River, which is characterized by a mixed composition of granitic and pheissic bedrock, while zone 4 is influenced by the Dongjin River, which has granitic bedrock (Figure 2). Kaolinite (ML), a clay mineral with the lowest activity and compressibility, is generated from primary minerals such as feldspar. In zone 4, located in the lower reaches of the Dongjin River, the predominance of granitic bedrock contains a high proportion of feldspar, a source mineral for kaolinite, resulting in low activity and high stability. In contrast, the Mangyeong River is influenced by a mixed bedrock of granite and pheiss. Gneiss, formed through metamorphism, contains a higher proportion of intergrade clay minerals comprising illite and smectite, which are more active than kaolinite. As shown in Figure 7, zone 1, located in the lower reaches of the Mangyeong River, had higher proportions of illite (CL) and montmorillonite (CH), a highly compressible clay mineral, compared with zone 4. The high activity and compressibility of illite and montmorillonite resulted in data heterogeneity, affecting the model's prediction accuracy and resulting in a minor decrease in R\({}^{2}\). Figure 7: The proportion of clay minerals by zone. (**a**) Zone 1; (**b**) zone 2; (**c**) zone 3; (**d**) zone 4. In the case of zone 3, being influenced by both water systems, it exhibited a middle proportion of kaolinite compared with zones 1 and 4. In zones 1, 3, and 4, a higher prediction accuracy was observed as the proportions of highly compressible and active clay minerals such as CL (illite) and CH (montmorillonite) decreased. This confirms the correlation between the characteristics of the clay minerals and model performance. However, in zone 2, as shown in Figure 6, the distribution of a thick clay layer combined with the presence of a shuice gate connected to the open sea has led to a mixture of external suspended sediments and clay layers deposited from the two water systems. This has resulted in high heterogeneity in the soil data, leading to poor model performance. The overall characteristics of each zone are presented in Figure 8.  
As such, zones 1, 3, and 4, classified based on the geotechnical and hydrogeological characteristics, demonstrated a superior model performance compared with the predictions based on the entire dataset for the Saemangeum area. Notably, even with significantly smaller training datasets, sufficiently reliable model performance could be ensured by considering the geotechnical and hydrogeological characteristics. Hence, when developing models to predict the compression index of soft ground, region-specific data preprocessing should take precedence over simply increasing the quantity of data, as this approach ensures relatively stable and reliable results. Consequently, the development of ML models for predicting the compression index of soft ground relies heavily on the distribution of data informed by the hydrogeological characteristics. When applying the average compression index values to large construction sites, it is essential to not only consider practical data acquisition, but also determine the influence range by incorporating the surrounding environmental factors including hydrogeological characteristics.