A new system for analyzing rivers, weather and sensor data could help provide extra warning time in advance of floods and improve disaster prevention...

A new system for analyzing rivers, weather and sensor data could help provide extra warning time in advance of floods and improve disaster prevention and preparedness efforts.

Researchers from IBM and the University of Texas (UT) at Austin have used the advanced analytics system to predict the behavior of Texas’ Guadalupe River at more than one hundred times its normal speed. The IBM technology, aimed at flood prediction, 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 the software with advanced weather simulation models, municipalities and disaster response teams could make emergency plans and pinpoint potential flood areas on a river, according to IBM.

“Effective flood preparedness can be looked at as a large scale computing problem, with a huge number of relevant data and interdependencies,” said Frank Liu, research staff member at IBM Research, 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.”

Floods are the most common natural disaster in the United States. Traditionally, though, flood prediction methods 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.

The IBM-UT-Austin team is currently using the modeling technology to predict the behavior of the entire 230 mile-long Guadalupe River, as well as more than 9,000 miles of tributaries in Texas. In a single hour, the system can currently generate up to 100 hours of river behavior.

“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 at UT-Austin’s 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.”

That speed could be critical for smaller-scale river problems, such as urban and suburban flash flooding caused by severe thunderstorms. Within the emergency response network in Austin, Texas, professors from the university are linking the river model directly to NEXRAD radar precipitation to better predict flood risk on a creek-by-creek basis.

According to IBM, 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.