# Prediction of Water Leakage in Pipeline Networks Using Graph Convolutional Network Method
## 2 Materials and Methods
### Experimental Setup  
This study conducted an experimental investigation to obtain the necessary datasets for the proposed GCN and SVM algorithms, aiming to predict pipeline leaks. The experimental design and implementation phase consisted of four main steps, which are provided below. * Research Design
* Selection of Data Collection Methods
* Data Collection Process
* Data Recording  
During the research design phase, the required data to be collected (such as pressure and flow rate) were determined. The experimental dataset was prepared to be ready for data collection following the created leak scenario, and the sensors were calibrated. The limitations and constraints of this research were identified. The limitations of this experimental research are as follows:  
* The system is assumed to be represented by eight pressure sensors and two flow meters. * It is assumed that the experimental setup and measurement instruments are unaffected by external factors, such as heat, vibration, noise, and light. * It is assumed that the characteristics of the region where the experimental setup is located, such as altitude, do not affect the precision of the measurements. \begin{table}
\begin{tabular}{c c} \hline \hline
**GCN Model Parameters** & \\ \hline Layer sizes & (16, 16) \\ Epochs & 70 \\ Optimization method & Adam (lr = 0.01) \\ Loss function & Categorical cross-entropy \\ Activation functions & Tanh, Softmax \\ Drop out & 0.4\% \\ Model train/rest rate & 0.24\% \\ Verbose & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the GCN model. \begin{table}
\begin{tabular}{c c} \hline \hline
**SVM Model Parameters** & \\ \hline C & 1.0 \\ Break ties & False \\ Cache size & 200 \\ Class weight & None \\ Coef0 & 0.0 \\ Decision function shape & ovr \\ Degree & 3 \\ Gamma & scale \\ Kernel & RBF \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters of the SVM model. It is assumed that the PVC pipe type used in the experimental setup represents other pipe types used in transmission lines. The limitations of the experimental research are as follows:  
The findings are limited to the data set obtained from the experimental setup. The representation of liquid fluids is limited to water, used as the fluid in the experimental setup. The pressure measurements are limited to the eight pressure sensors, two flow meters, and PVC pipes used in the experimental setup. An experimental setup was designed to create data sets required to operate graph-based machine learning algorithms. A leakage scenario, which is detailed in the titled scenario, was made to represent possible leakage situations. Pressure and flow variables were measured over time through pressure sensors (MPS 500) and flowmeters using the experimental setup shown in Figure 1 for data collection. The data collection method and data recording processes were planned. During the selection of data collection methods, the nature and volume of the data were considered to determine how the data would be collected. In the data collection process, considerable attention was paid to collecting the data accurately and consistently. A 12-channel data acquisition card was used for recording the data. The data acquisition card records ten data points per second. The recorded data were transferred to electronic spreadsheets, and the accuracy and integrity of the data were checked. Any instances of incorrect or missing data were identified and corrected. For graph-based machine learning algorithms to work, the obtained data must be represented by nodes and edges that connect these nodes. In the experimental dataset, the pressure sensors represent the nodes, and the connections between these sensors represent the edges. A total of eight pressure sensors were used in the dataset. Since the  
Figure 1: Experimental setup for pressure and flow measurement in the pipeline.