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\begin{document}

\title{A reduced complexity model with graph partitioning for rapid hydraulic
assessment of sewer networks}



\author[1]{Barnaby Dobson}%
\author[1]{Hannah Watson-Hill}%
\author[1]{Samer Muhandes}%
\author[2]{Morten Borup}%
\author[1]{Ana Mijic}%
\affil[1]{Imperial College London}%
\affil[2]{Technical University of Denmark}%


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  \date{May 27, 2024}


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\begin{abstract}
Existing tools for sewer network modelling are accurate but too slow for
a range of modern applications such as optimisation or uncertainty
analysis. Reduced complexity sewer network models have been developed as
a response to this, however, current applications are slow to set up and
still require high-fidelity models to be run for calibration. In this
study, we compare and develop graph partitioning techniques to
automatically group sections of sewer network into semi-distributed
compartments. These compartments can then be simulated without
calibration in the integrated modelling framework,
CityWat-SemiDistributed (CWSD), which has been developed for application
to sewer network modelling as part of this study. We find that combining
graph partitioning with CWSD can produce accurate simulations 100-1,000x
more quickly than existing high-fidelity modelling. We compare a range
of graph partitioning techniques to enable users to specify the level of
spatial aggregation of the partitioned network, also enabling them to
preserve key locations for simulation. We test the impact of temporal
resolution, finding that accurate simulations can be produced with
timesteps up to one hour. Our experiments show a log-log relationship
between temporal/spatial resolution and simulation time, which would
enable a user to pre-specify the speed and accuracy needed for their
application. We expect that the speed and flexibility of the approach
presented in this work may facilitate a variety of novel applications of
sewer network models ranging from continuous simulations for long-term
planning to spatial optimisation of network design.%
\end{abstract}%



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