Solving stormwater overflows through spatial catchment optimisation

Location:

Department of Engineering & Department of Computer Science, University of Exeter (Streatham Campus).

The University of Exeter’s is inviting applications for a fully funded PhD studentship to commence on 23 September 2024 or as soon as possible thereafter. For eligible students the studentship will cover Home or International tuition fees plus an annual tax-free stipend of at least £19,237 for 3.5 years full-time, or pro rata for part-time study. The student would be based in Engineering (but work closely with Computer Science) in the Faculty of Environment, Science and Economy at the Streatham Campus in Exeter.

International applicants: Students who pay international tuition fees are eligible to apply, but should note that you will have to cover the cost of your student visa, healthcare surcharge and other costs of moving to the UK to do a PhD.

Project Description:

The aim of this PhD project is to reduce stormwater spills and flooding by developing an AI/ Machine Learning driven optimisation tool. This tool will enable research to identify catchment-scale stormwater management strategies using readily available data.

Storm water inflow to combined sewer systems reduces network capacity and increases the risks of environmental pollution, overflows, treatment costs and flooding. Risk is exacerbated by climate change, urban growth and an increasing regulatory and public spotlight on stormwater management.

To manage this exacerbated risk, we need to significantly boost capacity in the existing pipe network, however the traditional methods for achieving this by replacing/ upsizing underground pipes and storage can be expensive and disruptive. In response to this, contemporary stormwater management has increasingly proposed adding solutions outside of the network using green infrastructure, storage and SuDS. However, despite established technical understanding of how to build these solutions, their application remains fragmented and ad-hoc due to an inability to strategically screen and apply them systematically at a catchment scale.

This project will connect understanding of site focused storm water interventions towards a systematic catchment scale strategy through exploring and developing spatial optimisation methods using machine learning approaches. This will provide a more effective and synergistic implementation of interventions through optimising placement and configuration across a catchment.

The method developed to spatially optimise catchments will also contribute a novel scientific resource, unlocking potential for extensive future application to answer fundamental questions about how we can integrate and apply spatial solutions to manage urban water at scale.

This project will be supervised by Dr James Webber (supporting water engineering) and Professor Ed Keedwell (supporting ML implementation).

For further information and to apply click here