# Prediction of Water Leakage in Pipeline Networks Using Graph Convolutional Network Method
## 4 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**System Status** & **Precision** & **Recall** & **f1-Score** & **Sample** \\ \hline Normal & 1.00 & 0.82 & 1.00 & 120 \\ Risky & 1.00 & 1.00 & 0.94 & 120 \\ Leakage & 0.90 & 0.89 & 0.95 & 120 \\ Accuracy & & & 0.94 & 360 \\ Macro Average & 0.97 & 0.96 & 0.96 & 360 \\ Weighted & 0.97 & 0.96 & 0.96 & 360 \\ Average & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of classification of scenarios according to the GCN algorithm. Figure 5: A clustered graphical representation of the pressure values of the sensors in Table 5, grouped according to their similarities. The classification of the predicted values of the GCN algorithm for the system's risky, leakage and normal states is shown in Figure 7, which was created using the t-SNE (t-Distributed Stochastic Neighbor Embedding) technique, which is employed for visualizing high-dimensional data by assigning each data point a location on a two- or three-dimensional map [43]. Figure 6: Accuracy and loss graph for the GCN algorithm. Figure 7: Classification of the system’s risky, leakage, and normal states with GCN. The X\({}_{1}\) and X\({}_{2}\) axes in Figure 7 represent the coordinates of the two-dimensional space generated by the t-SNE algorithm. The positions of the points on the graph are determined based on the similarities among the data points. The nodes labeled 1 and 2 within the circles in Figure 7 show the confusion of the algorithm in predicting leakage and risky situations, indicating that the nodes belonging to these situations have similar characteristics. The confusion matrix comparing the actual and predicted values of the GCN algorithm for the system's risky, leakage, and normal states is shown in Figure 8.  
The matrix compares the actual states with the predicted states obtained from applying the GCN algorithm in Figure 8. The findings showed that the system confused the risky situations with the leakage situations. The performance of the Graph Convolutional Network (GCN) algorithm, used for machine learning on graph-structured data, was evaluated by employing a classical machine learning algorithm, Support Vector Machine (SVM), for predicting leakages in pipeline systems using the same scenario and dataset. The parameters related to the performance of the SVM algorithm are determined as an accuracy score of 0.8666, a loss value of 0.1333, and an f1 score of 0.8667. The summary of the classification of scenarios according to the SVM algorithm is given in Table 7.  
For the leakage situations in Table 7, the f1-score is detected as 81%, the detection of risky situations with 82% accuracy, and the detection of normal situations with 100% accuracy. The system's average leakage, risk, and normal status are detected with an 87.66% accuracy rate. The SVM algorithm, which solely relies on node features and does not utilize edge features, has fewer samples than the GCN algorithm. The confusion matrix comparing the actual and predicted values of the SVM algorithm for the system's risky, leakage, and normal states is shown in Figure 9. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**System Status** & **Precision** & **Recall** & **f1-Score** & **Sample** \\ \hline Normal & 1.00 & 1.00 & 1.00 & 25 \\ Risky & 0.87 & 0.77 & 0.82 & 35 \\ Leakage & 0.76 & 0.87 & 0.81 & 30 \\ Accuracy & & & 0.87 & 90 \\ Macro Average & 0.88 & 0.87 & 0.87 & 90 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of classification of scenarios according to the SVM algorithm. Figure 8: Confusion matrix according to GCN results. The matrix compares the actual states with the predicted states obtained from applying the SVM algorithm in Figure 9. The findings showed that the system confused the risky situations with the leakage situations.