## 5 Conclusions  
This study aimed to predict the soil compression index by employing machine learning techniques that incorporate the geotechnical and hydrogeological factors, while providing a novel perspective for interpreting the results. The research focused on the Saemangeum reclaimed land, located on the west coast of South Korea, applying ML methods that incorporate geotechnical and hydrogeological factors to enhance the performance of the soil compression index prediction models.  
1. Predicting the compression index of zones classified based on the geotechnical and hydrogeological characteristics resulted in improved model performance and prediction accuracy compared with using the entire dataset.
2. The silt and clay content of the soil significantly affected the performance of the ML models. Zone 2, which had the thickest clay layer (composed of clay and silt), experienced the lowest prediction accuracy due to its minimal hydrogeological influence and significant exposure to open-sea effects.
3. The type of clay minerals significantly affected the performance of the ML models, and differences in the composition of clay minerals in the soil led to differences in the prediction accuracy. Notably, zones 1, 3, and 4 exhibited a higher R\({}^{2}\) compared with the predictions using the entire dataset. Additionally, zones influenced by a single water system (zones 1 and 4) demonstrated improved prediction performance, making the use of LR models more suitable. In contrast, zones influenced by multiple water systems (zones 2 and 3) showed relatively lower performance, where the use of MLR models was more appropriate.
4. When developing machine learning models to predict the compression index for large-scale sites, it is essential to define the influence range based on the hydrogeological characteristics of the target area and perform data preprocessing in accordance with the data distribution. Subsequently, the optimal design parameters must be calculated for each defined influence range.  
This study confirmed that incorporating the geotechnical and hydrogeological characteristics into the data preprocessing process can enhance the performance of ML models for predicting the compression index. However, this study focused its analysis primarily on the two main water systems, the Mangyeong River and the Dongjin River. Future studies should explore the impact of more detailed hydrological and geological characteristics on the model performance to further refine the prediction accuracy. In addition, AI has the potential to effectively analyze and train on complex and extensive data in the geotechnical field, however, the geotechnical field often involves relatively small datasets. Therefore, this study aimed to develop AI models capable of maintaining high accuracy even with limited datasets, thereby enhancing the overall accuracy and safety.  
Additionally, this study explained the characteristics of the sediments in the Mangyeong and Dongjin Rivers based on the general weathering properties of the bedrock. However, the soil formation of a parent rock is influenced by numerous factors including the region's climate, topography, and vegetation. Considering these factors could provide a more precise perspective of how the hydrological and sedimentary characteristics affect the performance of ML models.  
Conceptualization, S.R., J.K. and J.H.; Methodology, S.R.; Software, S.R.; Validation, S.R., J.K. and J.H.; Formal analysis, S.R. and J.K.; Investigation, S.R.; Resources, H.C.; Data curation, S.R.; Writing--original draft preparation, S.R.; Writing--review and editing, J.K., J.L. and J.H.; Visualization, S.R.; Supervision, J.H.; Project administration, J.K.; Funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.