• Multi-Objective Multi-Gene Genetic Programming for the Prediction of Leakage in Water Distribution Networks 

  Hayslep, M; Keedwell, E; Farmani, R (Association for Computing Machinery (ACM), 2023)

  Understanding leakage is an important challenge within the water sector to minimise waste, energy use and carbon emissions. Every Water Distribution Network (WDN) has leakage, usually approximated as Minimum Night Flow ...

• Understanding district metered area level leakage using explainable machine learning 

  Hayslep, M; Keedwell, E; Farmani, R; et al. (IOP Publishing, 1 January 2023)

  Understanding the various interrelated effects that result in leakage is vital to the effort to reduce it. This paper aims to understand, at the district metered area (DMA) level, the relationship between leakage and static ...

• Using Deep Ontologies in Formal Software Engineering 

  Brucker, AD; Ait-Sadoune, I; Méric, N; et al. (Springer, 15 May 2023)

  Isabelle/DOF is an ontology framework on top of Isabelle. It allows for the formal development of ontologies as well as continuous conformity-checking of integrated documents annotated by ontological data. An integrated ...

• Faster Federated Learning with decaying number of local SGD steps 

  Mills, J; Hu, J; Min, G (Institute of Electrical and Electronics Engineers (IEEE), 17 May 2023)

  In Federated Learning (FL) client devices connected over the internet collaboratively train a machine learning model without sharing their private data with a central server or with other clients. The seminal Federated ...

• Bi-objective design-for-control for improving the pressure management and resilience of water distribution networks. 

  Ulusoy, A-J; Mahmoud, HA; Pecci, F; et al. (Elsevier, 4 August 2022)

  This paper investigates control and design-for-control strategies to improve the resilience of sectorized water distribution networks (WDN), while minimizing pressure induced pipe stress and leakage. Both evolutionary ...

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