## 4 Model Prediction Results and Analysis
### Prediction Results
The LR and ENR models failed to produce results, likely due to the limited amount of training data, which prevented the models from accurately capturing the data distribution. Furthermore, from a hydrogeological perspective, zone 3 is influenced by both the Mangyeong River and Dongjin River, leading to a mixture of sediment characteristics. This could have hindered the model's ability to accurately learn the data patterns. Similar to zone 2, the MLR model demonstrated a relatively better performance, which is presumed to be due to the influence of the two water systems leading to mixed geological characteristics. For zone 4, located in the southern region, the R\({}^{2}\) values ranged from 0.831 to 0.8546, and the RMSE values ranged from 0.0424 to 0.0457, with minimal prediction accuracy differences among the models. The LR model with an alpha of 0.004 exhibited the highest accuracy. The best model with the highest R\({}^{2}\) value for each zone was selected, and the measured and predicted values of the compression index for each data point are presented in Figure 5. Additionally, the mean absolute percentage error (MAPE) values of all models were found to follow the same trend as the RMSE values. Figure 5: Actual and predicted values for best models. (**a**) Zone 1 (LR); (**b**) Zone 2 (MLR); (**c**) Zone 3 (MLR); (**d**) Zone 4 (LR). Considering the hydrogeological characteristics and sedimentary features of each zone, zones 1 and 4, characterized by the influence of a single hydrogeological characteristic, achieved higher prediction accuracy. In both zones, the LR model showed the best performance. On the other hand, zones 2 and 3, characterized by mixed hydrogeological influences, showed a lower prediction accuracy compared with zones 1 and 4, with the MLR model achieving a higher model performance than the LR model in these zones. Furthermore, when zones were classified based on hydrogeological characteristics (zones 1, 3, 4), they generally exhibited higher accuracy than the case where predictions were made without considering these characteristics (total), as evaluated by R\({}^{2}\). This result confirms the importance of classifying data based on site-specific characteristics when conducting machine learning-based predictions. Therefore, in areas characterized by mixed hydrogeological influences or insufficient training data, it is recommended to use the MLR model for predictions or to utilize the entire dataset. Conversely, in areas with clearly defined hydrogeological characteristics and simple influences, as well as abundant data, it is preferable to use the LR model or to implement the LR model after classifying the data based on geotechnical and hydrogeological characteristics. The four ML models used in this study have the advantage of expressing the relationship between dependent and independent variables in the form of equations. Regression equations derived from the best-performing models for each zone are presented in Table 4. The equations generated using the LR model (zones 1 and 4) showed relatively higher R\({}^{2}\) values compared with those generated using the MLR model (zones 2 and 3). In particular, the equation for zone 4 reflects the characteristics of the LR model, which eliminates less important features by setting their regression coefficients to zero. As a result, the natural water content (w\({}_{n}\)) was excluded from the equation of zone 4. This suggests that the natural water content is a relatively less significant feature for predicting the compression index in this zone. The exclusion of the natural water content feature can be attributed to the stable sedimentation activities in areas of the Saemangeum region that are unaffected by the influences of the inner and outer seas, where the impact of natural water content on the compression index is minimal. An analysis of the regression equations for the four zones revealed that, while the coefficients for all other soil properties consistently share the same sign, the sign of the LL coefficient varies by zone.