# Tunnel Geology Prediction Using a Neural Network Based on Instrumented Drilling Test
## 4 Tunnel Geology Prediction Performance
### Neural Network Model Performance Comparison  
The performances of the basic neural network and the deep neural network with GA optimization were compared based on their predictive accuracies and their receiver operating characteristic (ROC) curves. An ROC curve shows the performance of a prediction model at each threshold. Lowering of the threshold causes additional data to be marked as  
Figure 8: Deep neural network learning curve for 815 epochs.  
Figure 7: Deep neural network learning curve for 5000 epochs.  
positive. The X-axis shows the false positive rate, while the Y-axis denotes the true positive rate; these are defined as follows:  
\[False\ positive\ rate=\ \frac{False\ positive\ numbers}{False\ positive\ numbers\ +\ Ture\ negative\ numbers}, \tag{4}\]  
\[True\ positive\ rate=\ \frac{Ture\ positive\ numbers}{True\ positive\ numbers\ +\ False\ negative\ numbers}. \tag{5}\]  
With respect to the ROC curve, the area under the curve (AUC) serves as a general measure of predictive performance. The larger the area, the higher the model accuracy.  
Figure 9 presents the average ROC curves for both the basic neural network and the deep neural network model. The AUC of the deep neural network is larger than the AUC of the basic neural network; this finding indicates that the deep neural network model offers greater accuracy than the basic neural network.  
To analyze the performance of the neural network model for each rock type, a confusion matrix was constructed. A confusion matrix shows the number of correct and incorrect predictions for each rock type.  
Figure 10 shows the confusion matrix for the training data. The diagonal elements represent the count of rock units whose types were perfectly predicted, while the off-diagonal elements indicate the counts of incorrectly predicted rock types. The deep neural network model performed better than the basic neural network for every rock type, and it showed higher accuracy overall. The basic neural network performed well in predicting label 2 (i.e., limestone; 92.23% accuracy) because numerous examples of limestone were encountered. The basic neural network performed poorly in predicting label 1 (i.e., fractured limestone; 47.13% accuracy), label 3 (i.e., clay; 47% accuracy), and label 4 (i.e., soft interlayer; 63.22% accuracy) because relatively little few samples were encountered for these rock types. The deep neural network confusion matrix exhibited higher predictive accuracy than the basic neural network for label 1 (67.69% accuracy), label 2 (98.67% accuracy), label 3 (99.48% accuracy), and label 4 (88.40% accuracy). In both neural network models, label 1 showed a relatively low predictive accuracy compared with the other rock types. The percentage of fractured limestone units incorrectly predicted as limestone was approximately 45.20% for the basic neural network and 32.04% the deep neural network. These high misidentification  
Figure 9: Receiver operating characteristic (ROC) curves of the deep neural network and the basic neural network.  
rates were likely due to fractured limestone and limestone ultimately represent the same rock compositional type under different structural conditions.  
Figure 10: Confusion matrices: (**a**) basic neural network confusion matrix (**b**) deep neural network confusion matrix.