# Spatial Autocorrelation Incorporated Machine Learning Model for Geotechnical Subsurface Modeling
## 2 Study Area and Methodology
### Study Sites and Available Soil Investigation Data  
The soil investigation data used as a dataset in this study were acquired from the Saemangeum onshore solar farm area in Gunsan, Republic of Korea. The study area is approximately 3.5 km\({}^{2}\), and the data were acquired from 33 boreholes inside Zones A, B and C and from one borehole outside the area, as can be seen in Figure 2. The maximum distance between the test boreholes and its adjacent borehole is approximately 418 m (C-BH-1-C-BH-4). The dataset consists of test boreholes coordinate on the x, y, and z axes, the ground surface elevation, the groundwater level (GWL), the SPT-N value, and the soil test data such as percent finer than 0.075 mm sieve (No. 200) and the classification of soil samples according to USCS. The depth of the GWL at each borehole was identified to vary with values ranging from 0.9 m to 1.5 m. The drilling depth of these boreholes varied from 23.3 m to 32.5 m, wherein most of the boreholes were drilled up to depths of around 30.0 m. In the study area, three types of soil were identified according to the USCS soil classification system, which are sand with silty fines (SM), silt with a low liquid limit (ML), and clay with a low liquid limit (CL). Based on the soil investigation data, soil type within 0 m to 5 m of depth are mostly loose silty-sand soils. However, rock layers are also found within this range of depth at several locations. Geotechnical information within this depth range is important in choosing the appropriate foundation design that can securely support the load of the solar panel structure. Therefore, the constructed machine learning models in this study were expected to predict and identify geotechnical information and soil layers within 0 m to 5 m depth. Soil investigation data at slightly wider range of depths, within 0 m to 10 m depths, were used to train machine learning models that could cover prediction at the expected range of depth. From the previously mentioned dataset, all variables for spatial modeling can be summarized as (1) X coordinate, (2) Y coordinate, (3) Z coordinate, (4) ground surface elevation (GSE), (5) groundwater level (GWL), (6) measured SPT-N value, (7) percent finer than 0.075 mm sieve, and (8) USCS based soil classification. The statistical information such as means, median, and standard deviation of each variable are presented in Table 1. In addition, histograms to show the frequency distributions of each attribute are presented in Figure 3. A multitude of information can be acquired from the frequency distribution. For the X-Coordinate feature, most of the samples are available at the X axis with coordinates  
Figure 2: Study area, Saemangeum onshore solar farm, Gunsan, Republic of Korea. between 4200 and 4700, while the distribution of the Y-Coordinate feature is more balanced. For the Z coordinate feature, most of the data are available within 2 m to \(-\)7 m. Ground surface elevations of the study area are recorded mainly at elevations > 1.5 m and the groundwater levels are mostly recorded at elevation between 0.9 m and 1.5 m. SPT-N values for most of the samples are identified between 0 N and 16 N. The collected dataset has a balanced number of sand samples (SM, % finer than 0.075 mm sieve < 50) and silt-clay samples (ML and CL, % finer than 0.075 mm sieve \(\geq\) 50). For the Soil Classification feature, the dataset has more soil samples of SM and less soil samples of CL. The frequency distribution as presented in Figure 3 is also able to visualize the distribution skewness. It can be clearly seen that the Z coordinate and SPT-N data are negatively and positively skewed, respectively. The distribution of each attribute shows that the data are not in a proper form for training a machine learning model and need to undergo data pre-processing to make the data more computationally accountable and explicable [21].