Machine learning-equipped camera systems can be an effective and low-cost flood defence tool, researchers show.
Working with Dr Andrew Barnes, from the Department of Computer Science and a member of the Centre for Climate Adaptation & Environment Research, and Dr Thomas Kjeldsen, a Reader in the Department of Architecture & Civil Engineering and a member of the Centre for Regenerative Design and Engineering for a Net Positive World (RENEW), Dr Chris Rowlatt from the Institute of Mathematical Innovation, has help the researchers show that their AI-enabled detection software, ‘AI on The River’ trained to accurately detect natural debris, litter or waste blocking trash screens mounted in culverts, can be integrated to existing CCTV systems to provide an early warning of likely flooding.
The machine learning process created by the team is already attracting attention from flood prevention organisations in countries including South Africa, where monitoring equipment is available but data that could be used to train an AI to do the same job is scarce or not collected.
The paper, CCTV Image-based classification of blocked trash screens, is published today in The Journal of Flood Risk Management (DOI: 10.1111/jfr3.13038).