# Spatial Autocorrelation Incorporated Machine Learning Model for Geotechnical Subsurface Modeling
## 3 Machine Learning Model Prediction Results and Discussion
### Performance of Various Algorithms on the Geotechnical Subsurface Model  
This section analyzes the visualization of the prediction results from the machine learning models in two-dimensional space (X-Y plane). Analysis is needed to understand the behavior of each MLA on generating the subsurface geotechnical model. The analysis was performed by evaluating the predicted value of each target in the unobserved spaces within the boreholes. The predicted values in these spaces were examined to decide whether the values generated by the machine learning models are sensible or not. The high difference between the predicted value in the unobserved spaces and the value at adjacent observed points could indicate the low performance of the machine learning model. For the ANN based regression model (GSE, GWL, SPTN, and FINER model), the obvious distinction can be noticed in Figure 13c-1-c-4. The geotechnical subsurface model generated from the ANN algorithm tends to have more peak or depression filling the spaces between the boreholes in the prediction value contour map. The predicted values within these locations have a noticeable difference from the value at the adjacent observed data, especially for the SPT-N prediction. As can be seen in Figure 13c-1, a high ground surface elevation of 2.4 m is predicted within borehole B-BH-1, B-BH2, B-BH-4, and B-BH-5 while the values from the adjacent observed points are 1.7 m, 1.8 m, 1.9 m, and 2 m. The predicted value is slightly higher than the observed values close to it. Another example can be seen in Figure 13c-2, wherein larger values exist at the space within boreholes A-BH-1, A-BH-2, A-BH-6, A-BH-8, and A-BH-9 for the groundwater level prediction. The predicted value at the area is 0.4 m, while the values at the adjacent observed points are \(-\)0.3 m, \(-\)0.3 m, \(-\)0.1 m, \(-\)0.3 m, and \(-\)0.2 m. An elevation difference by about 0.5 m can be seen here while the groundwater level close to the query point is under 0 m. The huge discrepancy can be seen in Figure 13c-3, wherein the ANN model for SPT-N value prediction shows a poor prediction performance at elevations 0 m and \(-\)3 m. The model predicts a high value of SPT-N of 50 in the space within borehole A-BH-1, A-BH-2, C-BH-1, C-BH-4, and C-BH-5. However, the actual data show that the high SPT-N value \(>\) 40 N does not exist until elevation \(-\)4.4 m (at the borehole A-BH-2). The GPR- and ANN-based model shows little difference in SPT-N value formation along the depth as can be seen in Figure 13b-3,c-3. The GPR model as presented in Figure 13b-3 shows a similar behavior with the ANN model, where some peaks are noticed in the unobserved spaces close to the observed data points. Several peaks exist such as the peak value close to C-BH-5 and the peak within B-BH-6, B-BH-9 and B-BH-12. The predicted SPT-N value at both peak differs hugely from the SPT-N value at the closest observed points (\(>\)20 N), which makes the prediction is not sensible. The GPR model for predicting percent finer than 0.075 mm sieve shows the most sensible result, as it predicted values at unobserved area closer with the values at observed points. For example, the predicted value at elevation \(-\)4 m around A-BH-1 is 32%, while the value at the adjacent observed point is 32.3%. For the soil classification models, all algorithms generate quite similar results as shown in Figure 13a-5-c-5. The generated models could follow the formation of a percent finer than 0.075 mm sieve, wherein a low value of less than 50% is classified as SM, and the higher value of greater than 50% is classified as ML and CL. The SC_kNN_EDF_NOPCA model can be employed directly for the prediction of soil classification. The analysis provided in this section demonstrates that the trained models with low RMSE or MAE score on the test data do not guarantee that the model can generate sensible spatial prediction models. It is important to visualize the spatial predictions to visually examine the sensibility of the predicted value.