A Novel Framework of Constructing on Water Pipeline Networks by Node Embedding Approach

The increasing annual demand necessitates an efficient model for planning large-scale networks, which typically include numerous nodes and edges. Designing an operational pipeline that efficiently utilizes resources presents significant challenges. This study presents a graph-based approach for designing a water pipeline network, with graph edges representing pipelines and nodes representing users (demand) or reservoirs. To address this complexity, we use Graph Embedding approach, a machine-learning technique that compresses large-scale graphs into lower-dimensional vector spaces. This method employ a random walk model to make sequence of nodes with their neighborhoods and a skip-gram model train them to ensure accurate modeling of the shortest paths between them. Then, the nodes are clustered in vector space and optimized using the minimum spanning tree(MST) method. The graph generated still preserves essential network characteristics such as node relationships and spatial proximity. The resulting model produces a graph based on the geographic coordinates of nodes that map the layout of the water pipeline network. In forming large-scale network layouts, the graph embedding approach improves the computation time efficiency. We evaluate the model's performance using the GFS-Score and validate the network layout with EPANET. This approach offers significant potential in advancing the development of intelligent water network structures for large-scale complex networks.