Researchers have created a new simulation model that can predict flooding during an ongoing disaster more quickly and accurately than is currently possible. University of Melbourne researchers have created a new simulation model that can predict flooding during an ongoing disaster more quickly and accurately than is currently possible.
Researchers say the new model has significant potential benefits for emergency response, reducing flood forecasting time from hours and days to seconds and allowing flood behavior to be accurately predicted as an emergency unfolds.
University of Melbourne Ph.D. 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.
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.
Rory Nathan
The LSG model can generate predictions that are as accurate as our most advanced simulation models, but at 1000 times faster speeds. According to Professor Nathan, the development has tremendous potential as an emergency response tool.
“At the moment, our most advanced flood models can accurately simulate flood behavior, but they’re very slow and can’t be used during a flood event as it unfolds,” said Professor Nathan, who has 40 years of engineering and environmental hydrology experience. Prof. Nathan stated.
“This new model provides results a thousand times more quickly than previous models, enabling highly accurate modeling to be used in real-time during an emergency. Being able to access up-to-date modeling 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 tested on two vastly different yet equally complex river systems in Australia, the LSG model predicted floods with 99 percent 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 currently used advanced models.
The new model’s speed also enables responders to account for the significant unpredictability in weather forecasts. Due to the limitations of current flood forecast models, 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.
Professor Nathan stated that the model, the result of two years of development work, has a number of potential benefits in Australia and around the world.
“This new model may also be useful in assisting us in designing more resilient infrastructure.” “Being able to simulate thousands of different flooding scenarios, rather than just a few,” Professor Nathan explained, “will help design infrastructure that can withstand more unpredictable or extreme weather events.”
