Plain Language Summary
Analysis and improvement of urban water networks requires hydrodynamic
models. Since these models are computationally expensive, researchers
and engineers often resort to fast alternatives known as surrogate
models. With the rise of artificial intelligence, machine learning
methods have been increasingly used for surrogate modelling of urban
water networks. In this study, we thoroughly reviewed recent papers on
the field to outline the current state-of-the-art and propose future
research directions. While many successful applications already exist,
we found that these models have three main limiting factors: i) they
need large amounts of data, ii) they are not explainable, and iii) they
are too specific to each case. We argue that researchers can overcome
these limitations by considering recent advancements in artificial
intelligence and implement modeling techniques that better leverage the
structure of the underlying data. Other promising direction include
developing comprehensive benchmark databases and leveraging surrogate
models for more complex applications.
1 Introduction
Urban water networks (UWNs) comprise drinking water distribution and
urban drainage systems (WDS and UDS). The former are responsible for
supplying drinking water to cities and the latter for evacuating
wastewater and stormwater runoff. These infrastructures are a
fundamental part of the city and are directly linked to its development
(Brown et al., 2009). Each of these systems faces challenges to improve
and maintain quality service in a dynamic urban environment under a
widening range of climatic conditions; especially, in a climate-changing
situation. Designing, optimising, and intervening in these systems
requires approximating their hydraulic behaviour. Several models have
been developed in the past years for simulating UWNs. Traditional
modelling approaches are either based on accurate description of the
physical processes or rely on simplified conceptual approaches;
nonetheless, the former usually entail computationally expensive
calculations while the latter lack fidelity. Applications such as
optimisation, real-time modelling, and uncertainty analysis need an
efficient model for evaluating the performance of a system multiple
times or as fast as possible. Consequently, they require short execution
times while maintaining a sufficient level of detail.