A Dimensionality-Reduction Strategy to Compute Shortest Paths in Urban Water Networks
Carlo Giudicianni, Armando di Nardo, Gabriele Oliva, Antonio Scala,, Manuel Herrera

TL;DR
This paper introduces a dimensionality-reduction method for efficiently computing shortest paths in urban water networks, significantly reducing computation time while maintaining accuracy, and enabling near real-time monitoring and control.
Contribution
It proposes a novel community-based network partitioning strategy that simplifies water network models without losing key information for shortest path calculations.
Findings
Achieves over 50% reduction in computational time for shortest path calculations.
Maintains exact shortest path solutions despite network reduction.
Provides hydraulic and economic benefits through improved network monitoring.
Abstract
The efficient computation of shortest paths in complex networks is essential to face new challenges related to critical infrastructures such as a near real-time monitoring and control and the management of big size systems. In particular, using information on the minimum paths in water distribution networks (WDNs) allows to track the diffusion of contaminants and to quantify the resilience and criticality of the system. This is, ultimately, approached by considering dynamically changing path-weights that depend on the flow or on other information available at run-time. These analyses tipically include all the WDN assets but reducing the high degree of physical details with a minimum lost of key information for their performance assessment. This paper proposes a strategy to compute minimum paths that is based on a dimensionality-reduction process. Specifically, the network is partitioned…
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Taxonomy
TopicsWater Systems and Optimization · Internet Traffic Analysis and Secure E-voting · Anomaly Detection Techniques and Applications
