Metamodel reports details on the computational algorithm (e.g.,
ANNs, Support Vector Machines) used to replace the original simulator
along with further details on its architecture (i.e., deviations from a
hidden layer ANN). The type and number of input and output variables are
also reported to infer the dimensionality of the SM and the complexity
of the RS to approximate. As for the performance, we report the
computational speed-up provided by the metamodel and the fidelity to the
original simulation, usually approximated with an accuracy metric. These
criteria have been identified as the most relevant ones by previous
related studies (Broad et al., 2015; Razavi et al., 2012b).
Nevertheless, it is possible to consider other factors, such as
development time, robustness and explainability. While assessing these
criteria may enrich the analysis, they are not employed in most of the
surveyed papers, and they are thus not included in this review.
3 Review – Current status of Machine Learning Surrogate Models in Urban
Water networks
The analysis of the surveyed papers show an increase in research
activity between 2015 and 2020 with approximately two-thirds of the
manuscripts published during this period. In terms of application, most
of these papers are related to optimisation. For the case study, there
is a noticeable difference between WDSs and UDSs since the latter
networks lack the use of benchmark cases. Regarding the metamodel, the
most popular algorithm is the fully connected ANN; because of this, we
report the details of the used metamodel as deviations from a standard,
one hidden layer, fully connected ANN, also referred to as simple
Multi-layer perceptron (MLP). Table 2 summarizes the extracted
information of the reviewed papers arranged in the previously mentioned
categories: purpose, case study, and metamodel.