# Spatial Autocorrelation Incorporated Machine Learning Model for Geotechnical Subsurface Modeling
## 3 Machine Learning Model Prediction Results and Discussion
### Performance Evaluation of Constructed Machine Learning Models
The highest prediction error by all models does not exceed 0.6 m. However, the GWL_ANN_XY_NOPCA and GWL_GPR_EDFB models show a larger prediction error and are considered worse in performance compared to the GWL_GPR_EDF_90 model. For the SPT-N value predictor model, both SPTN_ANN_EDF_80 and SPTN_ANN_XY_NOPCA models performed quite well for  
Figure 6: Regression EDF, XY, and EDFB machine learning model performance quantified by RMSE and MAE score for **(a)** GWL, **(b)** GWL, **(c)** SPTN, and **(d)** FINER model. The color legends indicate different form of Location Information modeling and the different status of PCA applied on the models. predicting SPT-N value under 10 blows. The performance gradually decreases for both models as the SPT-N value increases. The highest error identified from the test data is 31 N-value and 41 N-value of blows for SPTN_ANN_EDF_80 and SPTN_ANN_XY_NOPCA models, respectively. For SPTN_SVM_EDFB model, the prediction performance is not quite satisfying as the model produces higher prediction error, even for the low SPT-N value < 10 blows. The highest prediction error identified from the test data is 36 N-value for this model. From this result and RMSE-MAE score comparison, the SPTN_ANN_EDF_80 is considered a better model for predicting SPT-N with high reliability for predicting lower SPT-N value until \(\pm\)10 N of blows and low reliability in predicting higher SPT-N value. The FINER_GPR_EDF_NOPCA model shows an overall good performance for predicting the percent finer than 0.075 mm sieve. The identified highest prediction error does not exceed 13%. This performance is better compared to the FINER_ANN_XY_NOPCA and FINER_GPR_EDFB model, with highest error identified as 20.3% and 46.7%, respectively. Therefore, the FINER_GPR_EDF_NOPCA model is considered to be better for predicting percent finer than 0.075 mm sieve. A different approach was applied to analyze the performance of classification machine learning model in this study for predicting the soil classification based on USCS. First, the performance analysis was performed by examining the confusion matrix presented in Figure 9. It clearly shows that all models with EDF, XY, and EDFB modeling approaches provide satisfactory results in predicting soil classification. The SC_ANN_EDF_80 model only misclassified one data point, wherein it classified the ML soil class as CL. The SC_ANN_XY_NOPCA model misclassified two data points, wherein it classified the SM soil class as ML, and ML soil class as CL, and SC_ANN_EDFB model misclassified two data points, wherein it classified SM and CL soil class as ML. Figure 7: Classification EDF, XY, and EDFB machine learning model performance quantified by prediction accuracy for SC models. The color legends indicate different form of Location Information modeling and the different status of PCA applied on the models. Figure 8: Comparison of the best-performing (**1**) EDF, (**2**) XY, and (**3**) EDFB machine learning model: (**a**) GSE; (**b**) GWL; (**c**) SPT-N value; and (**d**) % finer than 0.075 mm sieve. Precision, recall, and F1 score, as presented in Figure 10, emphasize the result obtained from the confusion matrix. All three performance indicators show a high score > 95% for soil classification SM, ML, and CL. These high scores indicate that all machine learning models can perform balance classification, which means that they predict all types of classes equally well without tendency to perform good classification on some classes and poor on the others. With slightly better performance, SC_ANN_EDF_80 model was considered better to predict soil classification based on USCS in the study area. The entire best-performing model for the EDF, XY, and EDFB approaches and their number of PCs after the dimensionality reduction process using PCA are listed in Table 4. Overall, the superiority of \"EDF model\" in predicting geotechnical properties and soil classification can be noticed from the RMSE, MAE score, and the prediction accuracy of the chosen models.