A team of engineers and scientists from the University of Exeter has developed a model to predict when and where flooding will occur, 1000 times faster than existing flood protection systems.

The model uses artificial intelligence to ‘learn’, in the same way that biological neural networks in the human brain process data.

Designed for urban areas, the system can provide instant updates as bad weather conditions unfold. The model uses information about the drainage and sewage systems to predict the volume and flow of flood water in real time. Although not yet in general use, tests show a good ability to predict flooding with field trials and it is hoped that the model will soon be rolled out nationwide.

Professor Dragan Savić, who headed the development of the new model at the University of Exeter said:

“Our model can be trained to use data from rainfall events to distinguish between urban areas that suffer from flooding and those that don’t. Once it has learnt, it can then be used to classify new rainfall events into those likely to cause flooding and those that do not pose a threat.”

In a report produced by consultancy HR Wallingford, the model was tested against recorded rainfall events in London, Dorchester and Portsmouth and found that the model accurately predicted the actual outcomes.

The new model uses artificial neural networks, inspired by biological neural networks in the human brain. These are adaptive systems that can change their structure during learning and are ideal for modelling complex relationships like rain and flooding. Running repeated cycles of mathematical equations in the model simulates learning and results in dramatically faster and more accurate flood prediction than possible with existing systems.

With climate change likely to cause further severe weather events in the coming years, methods of quickly predicting flooding will become increasingly important.