FORECASTING WATER LEAKS IN PIPELINE NETWORKS WITH THE GRAPH CONVOLUTIONAL NETWORK APPROACH
P Mysak, I Mysak - European Science, 2024 - desymp.promonograph.org
P Mysak, I Mysak
European Science, 2024•desymp.promonograph.org… This study proposes an approach leveraging graph data structures to represent pipeline
networks effectively, utilizing GCN as a graph-based machine learning model [7]. GCN can
capture and account for the interdependencies between nodes and edges, enhancing
accuracy and predictive capabilities tailored to … This study aims to predict leaks by
monitoring pressure drops in water-carrying pipelines, focusing on accurately detecting
pipeline leaks. In this research, the network structure of pipelines is represented using …
networks effectively, utilizing GCN as a graph-based machine learning model [7]. GCN can
capture and account for the interdependencies between nodes and edges, enhancing
accuracy and predictive capabilities tailored to … This study aims to predict leaks by
monitoring pressure drops in water-carrying pipelines, focusing on accurately detecting
pipeline leaks. In this research, the network structure of pipelines is represented using …
Abstract
This study aims to predict leaks in water-carrying pipelines by monitoring pressure drops. Timely detection of leaks is crucial for prompt intervention and repair efforts. In this research, we represent the network structure of pipelines using graph repre
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