Will artificial intelligence help reduce the flooding we're seeing?
A new study by the University of Bath has found a potentially cost-effective way of spotting problems in the early stages
There's calls for CCTV systems to use artificial intelligence to help spot potential blockages and reduce flooding.
At the moment thousands of drainage culverts are monitored manually across the UK by camera and a trial in Cardiff showed artificial intelligence spotted the warning signs of problems much earlier and could then send an alert.
Dr Andrew Barnes from the University of Bath says by the time a litter build up is noticed it's often already too late.
The lecturer in Bath’s Department of Computer Science and a member of the Centre for Climate Adaptation & Environment Research, said: “We’ve been able to develop an efficient model that can capture and identify blockages before they become a problem – it’s proactive, so doesn’t wait for a flood to happen before raising the alarm.
“We’ve developed the system to be flexible and scalable – it could be applied almost anywhere, giving it huge potential in countries where flooding is an issue but where the resources to develop similar tools locally may be scarce.”
Focusing on a culvert site in Cardiff, the team used machine learning to train a camera system to automatically spot potential obstructions, resulting in it being able to identify likely blockages with close to 90% accuracy. In most cases in the UK, culverts are monitored manually over CCTV by local authority staff watching a bank of screens.
Dr Thomas Kjeldsen, a Reader in Bath’s Department of Architecture & Civil Engineering and a member of the Bath’s Centre for Regenerative Design and Engineering for a Net Positive World, added: “Climate change means the risk of flooding is growing all around the world.
"This work opens the potential for the development of new, light weight and cost-efficient flood management systems in urbanised areas, enabling authorities around the globe to adapt to the changing climate. This study is a first step toward a sustainable solution to flood forecasting, and it has opened a multitude of areas for exploration and exploitation.”
You can read the full study here