University of Melbourne researchers have developed a new simulation model, which can predict flooding during an ongoing disaster more quickly and accurately than currently possible, according to a press release from the University of Melbourne.
Published in Nature Water, the new model has potential benefits for emergency responses, reducing flood forecasting time from hours and days to just seconds, and enabling flood behavior to be accurately predicted at super-fast speeds as an emergency unfolds.
University of Melbourne PHD student Niels Fraehr, alongside Professor Q J Wang, Dr Wenyan Wu and Professor Rory Nathan, from the Faculty of Engineering and Information Technology, developed the Low-Fidelity, Spatial Analysis and Gaussian Process Learning (LSG) model to predict the impacts of flooding.
The model can allegedly produce predictions that are as accurate as our most advanced simulation models, but at speeds which are 1000 times faster.
Nathan said the development had enormous potential as an emergency response tool.
“Currently, our most advanced flood models can accurately simulate flood behaviour, but they’re very slow and can’t be used during a flood event as it unfolds,” said Nathan. “This new model provides results a thousand times more quickly than previous models, enabling highly accurate modelling to be used in real-time during an emergency. Being able to access up-to-date modelling during a disaster could help emergency services and communities receive much more accurate information about flooding risks and respond accordingly. It’s a game-changer.”
When put to the test on two vastly different yet equally complex river systems in Australia, the LSG model was able to predict floods with a 99% accuracy on the Chowilla floodplain in Southern Australia in 33 seconds, instead of 11 hours, and the Burnett River in Queensland in 27 seconds, instead of 36 hours, when compared to presently-used advanced models.
The speed of the new model also allows responders to account for the considerable unpredictability in weather forecasts. The limitations of current flood forecast models mean that simulations typically focus on the most likely scenario to predict flood.
By contrast, the LSG model developed by the researchers makes it possible to simulate how the uncertainty inherent in weather forecasts translates to on-the-ground flood impacts as a flood event progresses. The model uses mathematical transformations and a sophisticated machine learning approach to rapidly take advantage of enormous amounts of data whilst using commonly available computing systems.
Nathan said the model, which is the product of two years of development work, had a range of potential benefits in Australia and globally.
“This new model also has potential benefits in helping us design more resilient infrastructure. Being able to simulate thousands of different flooding scenarios, instead of just a handful, will help design infrastructure that holds up to more unpredictable or extreme weather events,” Nathan said. “As our climate becomes more extreme, it’s models like these that will help us all be better prepared to weather the storm.”