Graph-Based Methodology for Segment Criticality Assessment and Optimal Valve Placements in Water Networks

Continuous drinking water supplied by water distribution networks (WDNs) is essential for social well-being and economic development. WDNs are often divided by isolation valves into segments containing various elements, including nodes, pipes, tanks, and pumps. In the event of an element failure (e.g., pipe failure) within a segment, that segment can be isolated to facilitate repair. Effective failure management requires identifying critical valves and segments to minimize the number of affected users in such an event. Traditional hydraulic-based criticality analysis requires an hydraulic model to assess the criticality of isolation valves and segements, which can be time-consuming particularly for complex systems. Placing new valves also often requires data-intensive and time-consuming optimization methods thatare typically impractical for small and medium-sized WDN operators. To address these challenges, this study introduces a graph-based method to assess and improve WDN resilience by evaluating the criticality of valves and segmentsand the placement of new valves. The approach first evaluates the criticality of isolation valves based on their impact on network performance. Then, it reduces segment criticality by strategically adding new valves to minimize unmet water demand during isolation events. To achieve this, the mathematical graph of a WDN is constructed based on GIS data where valves are considered as nodes and segments as weighted edges. Subsequently, graph-based segment failure magnitudes are calculated, and eigenvector centrality is used to rank valves based on their influence, considering the importance of connected valves. Then critical segments are identified, and the Louvain-based community detection technique is used to determine the optimal placement of additional isolation valves. The method iteratively reassesses critical values to progressively reduce the criticality of both: segments and valves. The method was applied to a benchmark case study and a real WDN in an Alpine municipality in Austria. Results show a strong correlation (>0.9 Spearman) with hydraulic-based approaches. The developed approach effectively identified the most critical segments and valve, reducing the segment criticality by at least 40% and the number of critical valves to one fourth. These findings highlight the efficiency of community detection in valve placement and its potential to reduce both segment and valve criticality. This graph-based methodology is particularly beneficial for small-to-medium-scale WDNs lacking resources for hydraulic models.

Funding: The project “RESIST” is funded by the Austrian security research programme KIRAS of the Federal Ministry of Finance (BMF).