David Ayala-Cabrera

While operating a water distribution network (WDN), it is essential to prepare the system to face with intentional (e.g., cyber-physical attack) or unintentional (e.g., pipe leakage/burst) adverse events or other drivers such as the effects of climate change. Increasing the network’s preparedness to deal with anomalous events is an effective manner to improve the system’s resilience, reducing the negative impacts of events. In this paper, leakage/burst events, and ordinary network operation, are captured by both sensors and expert knowledge in a WDN in Spain. Event-driven and data-driven approaches are used to characterise the system behaviour, in particular when it is operating under the effects of an anomalous event, based on the resilience phases (i.e., absorptive, adaptive, restorative) for the collected dataset. The relationship of clustering pressure head time series based on their potential state in a particular resilience phase, in three random cases of short-term leakage events, was explored. This paper focuses on capturing the behaviour of the system, through the exploration of the hydraulic parameters of WDNs (in particular the pressure head) before, during, and after a leakage event, by means of a spatial-temporal analysis. It was observed that the network behaviour could be categorised into 1) ordinary operation and 2) during the event, which would allow to characterise the system behaviour when influenced by leakage/burst event and also explore its adaptability to resilience phases. The results show that it is possible to extract relevant patterns (i.e., feature maps) and generate an anomaly indicator from the pressure head heatmaps that facilitate the characterisation of anomalous events for WDNs.