Researchers from The University of Texas at Austin’s Cockrell School of Engineering and IBM have applied advanced analytics to river systems, weather and sensor data, to predict the Guadalupe River’s behavior more than a 100 times the normal speed. Simulating thousands of branches at a time, this technology could help provide up to one week warning of a flood, allowing more time for disaster prevention and preparedness.
Floods are the most common natural disaster in the United States, but traditionally flood prediction methods have been focused only on the main stems of the largest rivers — overlooking extensive tributary networks where flooding actually starts, and where flash floods threaten lives and property.
IBM’s new flood prediction technology can simulate tens of thousands of river branches at a time and could scale further to predict the behavior of millions of branches simultaneously. By coupling analytics software with advanced weather simulation models, such as IBM’s Deep Thunder, municipalities and disaster response teams could make emergency plans and pinpoint potential flood areas on a river.
Within the emergency response network in Austin, professors from The University of Texas at Austin are linking the river model directly to NEXRAD radar precipitation to better predict flood risk on a creek-by-creek basis.
“Combining IBM’s complex system modeling with our research into river physics, we’ve developed new ways to look at an old problem,” said Ben Hodges, associate professor in the Civil, Architectural and Environmental Engineering (CAEE) Department and at the Center for Research in Water Resources. “Unlike previous methods, the IBM approach scales-up for massive networks and has the potential to simulate millions of river miles at once. With the use of river sensors integrated into Web-based information systems, we can take this model even further.”
As a testing ground, the team, which also includes CAEE Professor David Maidment, is applying the model to predict the entire 230 mile-long Guadalupe River and more than 9,000 miles of tributaries in Texas. In a single hour the system can generate up to 100 hours of river behavior. Speed on this scale is a significant advantage for smaller scale river problems, such as urban and suburban flash flooding caused by severe thunderstorms.
“Effective flood preparedness can be looked at as a large scale computing problem, with a huge number of relevant data and independencies,” said Frank Liu, research staff member at IBM Research in Austin. “Using advanced models to simulate the scores of tributaries of large rivers along with other relevant real-time information such as weather, we are better able to give people valuable advance notice of a flood.”
In addition to flood prediction, a similar system could be used for irrigation management, helping to create equitable irrigation plans and ensure compliance with habitat conservation efforts. The models could allow managers to evaluate multiple “what if” scenarios to create better plans for handling both droughts and water surplus.
The project is currently being run on IBM’s Power 7 systems, which accelerate the simulation and prediction significantly, allowing for additional disaster prevention and emergency response preparation.
