# Prediction of Water Leakage in Pipeline Networks Using Graph Convolutional Network Method
## 4 Results  
Data collected with eight different pressure sensors and flow meters on the experimental setup were labeled for normal, risky, and leakage scenarios for a 10-s time interval and sampled in Table 5. The samples of risk and leakage are significantly different from most other samples regarding characteristics [42]. The data in Table 5 are visualized as a graph in Figures 4 and 5. Each color seen in Figures 4 and 5 represents an individual pressure sensor within the experimental dataset, and their corresponding mappings are as follows: A0 (blue), A4 (red), A6 (orange), A9 (yellow), A3 (green), A2 (violet), A7 (brown), A8 (turquoise). Using graphs, Figure 4 displays the experiment data (Table 5) representing normal, leak, and risky scenarios. The central nodes represented in black show the time information. The colored nodes connected to the black nodes by edges, A0, A2,... and A9 represent the pressure information obtained from the sensors. The radii of the nodes vary according to the measured pressure values. In leak scenarios, i.e., in the first, seventh, and eighth central nodes, the node sizes are very small, as the pressure values are relatively lower than the normal scenarios. In risky scenarios, i.e., in the fourth and sixth central nodes, it is observed that the nodes shrink compared to normal scenarios. Figure 5 below illustrates the clustering of similar nodes (based on node and edge characteristics) using the Python NetworkX library for the values provided in Table 5, where the nodes are clustered closely together. Figure 3: Graph representation of the scenario created for pressure and flow measurement in the pipeline. As shown in Figure 5, the sensor values obtained under normal conditions are clustered in a different area than those obtained during leaks and risky situations. The nodes in Figure 5 are numbered 1, 2, 3, etc. For each period in Table 5, eight sensor or node values are generated. For time 0, the node numbers are 1, 2, 3,..., 8, and for time 1, the nodes continue from 9, 10, 11,..., 16. The leakage situations in Table 5 are given at times 1, 7, and 8. It can be observed that the sizes of the nodes corresponding to these periods, which fall between nodes 9 and 16, 57 and 64, and 65 and 72, have shrunk to a size that is too small to be observed. The parameters related to the performance of the GCN algorithm are determined as an accuracy score of 0.9420, a loss value of 0.0980, and an f1 score of 0.94. The summary of the classification of scenarios according to the GCN algorithm is given in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Time** & **A0** & **A2** & **A3** & **A4** & **A6** & **A7** & **A8** & **A9** & **Flow 1** & **Flow 2** & **System State** \\ \hline
0 & 8.5852 & 7.7438 & 8.0905 & 7.9858 & 8.1753 & 9.9996 & 7.0367 & 9.9996 & 0.6433 & 0.6253 & Normal \\ \hline
1 & 2.4679 & 2.7862 & 2. For the leakage situations in Table 6, the f1-score is detected as 95%, the detection of risky situations with 94% accuracy, and the detection of normal situations with 100% accuracy. The system's average leakage, risk, and normal status are detected with a 94.20% accuracy rate. The real-time monitoring of leakage situations in pipeline networks and the detection of leakage situations occur at the moment of leakage. This situation causes a waste of time and water resources during the intervention phase of leakages. It is crucial to detect leakage situations in pipeline networks at an early stage. The system status being labeled as risky indicates the need for intervention in case of leaks. The prediction of a possible significant leakage situation is made in advance. Small interventions or maintenance and repair can be addressed at this stage before the failure escalates. The graph of the accuracy and loss values of the GCN algorithm is given in Figure 6.