# Prediction of Water Leakage in Pipeline Networks Using Graph Convolutional Network Method
## 1 Introduction
Representing many complex systems encountered in real life theoretically can be challenging. These complex systems can be described using graph structures. Many structures, such as airline, banking, social, medical, and supply chain structures, can be expressed using graph structures. It is feasible to depict intricate systems through graphs, such as distribution networks with multiple pipeline convergence or divergence points. In the literature, pipeline networks have been represented using graphs, and the spatial correlations of the pipeline network have been captured using the GCN model [29]. In this study, pipelines carrying water have been represented using graphs. The pressure values at certain points on the pipeline were visualized over time by modeling and observing pressure drops. This study used the graph-based machine learning algorithm GCN to detect pipeline leakage situations. Pipeline networks are often not limited to a single pipeline; significantly, in systems like water distribution networks, there are structures where multiple pipelines converge or diverge. Representing these complex structures with graph data structures provides advantages compared to traditional data structures. Conventional machine learning models (SVM, ANNs) often overlook the inherent dependencies among nodes and edges in pipeline networks. However, it is crucial to acknowledge that a leakage occurrence in one pipeline can have cascading effects on interconnected pipelines. In light of this, this study proposes an approach that leverages graph data structures to effectively represent pipeline networks, accompanied by the utilization of graph convolutional networks (GCN) as a graph-based machine learning model. By incorporating GCN, the model can effectively capture and contain the interdependencies between nodes and edges, thereby enhancing the accuracy and predictive capabilities specifically tailored to the domain of pipeline networks. The studies on GCN in the literature generally focus on object recognition and classification. The GCN algorithm has achieved accuracy rates of 75% in urban pipeline networks [30], 94% in image processing [31], 88% in customer product recommendation [32], and 86% in fault detection on steam turbines [33]. This study aims to predict leaks by monitoring pressure drops in water-carrying pipelines, with a specific focus on accurately detecting pipeline leaks. In this research, we represent the network structure of pipelines using graph representations. The graph convolutional neural network (GCN) model is utilized. GCN offer an advantage in capturing the intricate relationships among connections in pipelines. The data required for the GCN algorithm were collected from a test set using experimental methods, wherein a leakage scenario was deliberately created. Two datasets, edge and node, were specifically constructed for this purpose. The performance of the GCN algorithm in leak detection was compared with existing studies in the literature. The primary objective was to evaluate the performance of GCN in graph-based machine learning and perform a comparative analysis with traditional machine learning methods, explicitly employing the Support Vector Machine (SVM) algorithm as a reference. Given the escalating demand for water resources and the concurrent decline in availability, preventing leaks in pipeline transportation systems has become paramount. Detecting leaks facilitates prompt intervention, maintenance, and repair, minimizing economic losses and mitigating environmental impact. The findings of this study highlight the efficacy of the GCN algorithm in water leakage detection and underscore the potential of graph-based machine learning in tackling intricate problems.