# Prediction of Water Leakage in Pipeline Networks Using Graph Convolutional Network Method
## 5 Conclusions  
When the literature was reviewed, it was found that machine learning algorithms have been utilized for fault diagnosis in pipeline systems, yielding accuracy rates ranging from 78.51% to 99%. However, it is worth noting that non-graph-based machine learning models, such as SVM, ANNs, RVM, CNN, SPSA, and WML, have been predominantly employed instead of graph-based machine learning models for predicting and detecting leaks in pipelines.  
In this study, the GCN model achieved a detection accuracy of 95% for leak conditions, 94% for risky situations, and 100% for normal situations. The SVM (Support Vector Machine) model was used as a reference to determine the GCN model's effectiveness. The SVM model achieved a detection accuracy of 81% for leaks, 82% for risky conditions, and 100% for normal conditions. Overall, when comparing the performance of the two models (considering leaks, risky conditions, and normal conditions), SVM achieved an accuracy of 87%, while GCN achieved 94% accuracy.  
Based on the findings, the following conclusions have been drawn:  
1. The results reveal important implications for water resource management, reduction of water losses and sustainable living.
2. The GCN model demonstrated high accuracy and performance in predicting pipeline water leaks, including leaks and risky conditions. The results highlight the effectiveness of the graph-based machine learning model. The high accuracy of the GCN model in detecting risky conditions is crucial for early leak detection and preventive maintenance planning. Situations labeled as risky indicate the need to act to prevent leaks. Thus, predicting a potential major leak can be made in advance. At this stage, it is aimed to solve the problem before it grows by performing small interventions or maintenance and repair.
3. Both GCN and SVM models exhibited equally good performance in detecting normal conditions.
4. The GCN model outperformed the SVM model when comparing the prediction results for leaks and risky conditions. The GCN algorithm's ability to analyze edge and spatial relationships made a difference in leak detection.  
The results indicate the potential of graph-based machine learning methods in solving complex problems such as detecting water leaks in pipelines. Based on the experience gained from this study, it is aimed to replicate this study with other graph-based machine  
Figure 9: Confusion matrix according to SVM results.  
learning algorithms, such as GraphSAGE, HinSAGE, RGCN GAT, SGC, PPNP, APPNP, and Cluster-GCN, given the detection of leak locations, and to predict leak and blockage situations in pipeline systems with gaseous fluids in further studies.  
Conceptualization, E.S.; formal analysis, H.Y.; investigation, E.S.; methodology, E.S.; project administration, H.Y.; writing--original draft, E.S.; writing--review and editing, H.Y. and E.S. All authors have read and agreed to the published version of the manuscript.  
This study was supported by a scientific research project numbered \"FDK-2023-10459\" by Marmara University within the scope of the doctoral thesis.  
Not applicable.  
The data that support the findings of this study are available upon reasonable request from the corresponding author.  
The experimental stages of this study were conducted using the experimental setup developed for this study at the Fault Diagnosis Laboratory of Marmara University, Faculty of Technology, Mechatronics Engineering Department in Turkey. I am indebted to Marmara University for providing the facilities to conduct the present study.  
The authors declare no conflict of interest.