Use of Machine Learning for the Smart Design and Real Time Control of Stormwater Storages
Creators: name: Maier, Holger
name: Thyer, Mark
name: Liang, Ruijie
Publisher: name: The University of Adelaide
Year: 2022
Subjects: classificationCode: 370704
subject: Surface water hydrology
classificationCode: 400513
subject: Water resources engineering

description: Invited plenary talk at NSW Stormwater Conference, franc.sydney 2022.<b>Urban stormwater systems are stressed and traditional solutions are expensive</b>Urban stormwater systems are under increasing stress due to
densification and climate change, often necessitating expensive pipe upgrades.
In addition to the high cost of these upgrades, they are often not feasible due
to practical constraints (e.g. proximity of other services, traffic disruption
etc.). An alternative is to reduce peak system flows via storage. Traditionally, such storage is placed at catchment outlets, requiring
significant volumes and space to be effective. <b>Smart Design and Real-time Control of Distributed Storages - A solution for future?</b><br>The benefits of a given storage
volume can be increased significantly by distributing a series of smaller
storages throughout catchments at strategic locations, as these not only reduce
peak flows locally, but can reduce the coincidence of hydrographs from
contributing sub-catchments. Given the large number of potential storage
locations and sizes, machine learning methods are used in this research to
identify combinations that minimise system peak flows at critical locations
under a range of design conditions. By adding controls to the outlets of these
storages and operating them in real-time during storm events, also using
machine learning methods, system peak flows can be reduced further. In
addition, such smart controls provide opportunities for stormwater re-use for
urban greening, as well as producing outflows that meet ecological flow
requirements. <b>Case Study Outcomes: Smart stormwater storage reduce peak flows and infrastructure costs</b><br>The above approach to the smart design and real-time <br>control of
distributed stormwater storages is demonstrated for a case study in Unley,
South Australia. Results show that smart design of distributed storage can achieve significantly higher peak flow reductions than more commonly used
end-of-system storage. These range from 10% for 100 m<sup>3</sup> of system
storage up to 40% for 700 m<sup>3</sup> of system storage – whereas
end-of-system storage achieved no peak flow reduction for up to 700 m<sup>3</sup>
of storage. This reduced infrastructure costs by ~ 30%. The addition of optimised real-time control to distributed storages
is able to achieve an additional 10% in peak flow reduction. <b>References</b><br>Liang R., Thyer M.A., Maier H.R., Dandy G.C and Di Matteo M. (2021) Optimising the design and real-time operation of systems of distributed stormwater storages to reduce urban flooding at the catchment scale , Journal of Hydrology, 602, 126787, https://doi.org/10.1016/j.jhydrol.2021.126787Thyer M., Maier H., Di Matteo, M.(2019): Evaluating the Benefits of Smart Stormwater Systems: Unley Case Study. The University of Adelaide. Online resource. https://doi.org/10.25909/5de5971f2b4aeLiang R., Di Matteo M., Maier H.R., Thyer M.A. (2019) Real-time, smart rainwater storage systems: Potential solution to mitigate urban flooding (Open Access), Water, 11, 2428, DOI: 10.3390/w11122428, https://www.mdpi.com/2073-4441/11/12/2428<br>Di Matteo M., Liang R., Maier H.R., Thyer M.A., Simpson A.R., Dandy G.C. and Ernst B. (2019) Controlling rainwater storages as a system: An opportunity to reduce urban flood peaks for rare, long duration storms , Environmental Modelling and Software, 111, 34-41, https://www.sciencedirect.com/science/article/pii/S1364815218304857<br>