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Automatic Multiscale Approach for Water Networks Partitioning into Dynamic District Metered Areas


Water distribution systems (WDSs) today are expected to continuously provide clean water while meeting users demand, and pressure requirements. To accomplish these targets is not an easy task due to extreme weather events, operative accidents and intentional attacks; as well as the progressive deterioration of the WDS assets. Therefore, water utilities should be ready to deal with a range of disruption scenarios such as abrupt variations on the water demand e.g. caused by pipe bursts or topological changes in the water network. This paper presents a novel methodology to automatically split a WDS into self-adapting district metered areas (DMAs) of different size in response to such scenarios. Complex Networks Theory is proposed for creating novel multiscale network layouts for a WDS. This makes it possible to automatically define the dynamic partitioning of WDSs to support further DMA aggregation / disaggregation operations. A real, already partitioned, water utility network shows the usefulness of an adaptive partitioning when the network is affected by an abnormal increase of the peak demand of up to 15%. The dynamic DMA reuses the assets of the static partitioning and, in this case, up to the 82% of resilience is restored using 94% of the assets already installed. The results also show that the overall computational and economic management costs are reduced compared to the static DMA partition while the hydraulic performance of the WDS is simultaneously preserved.

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The authors wish to express their gratitude to Water Efficiency Network (http://www.watefnetwork.co.uk) for supporting this research.

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Correspondence to Carlo Giudicianni.

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Giudicianni, C., Herrera, M., di Nardo, A. et al. Automatic Multiscale Approach for Water Networks Partitioning into Dynamic District Metered Areas. Water Resour Manage 34, 835–848 (2020). https://doi.org/10.1007/s11269-019-02471-w

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  • Water distribution systems
  • Complex networks
  • Semi-supervised clustering
  • Dynamic operation and sustainable management
  • Abnormal conditions