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Texas A&M Researchers Develop Flooding Prediction Tool

The algorithm can predict the flow of floodwater during hurricanes and other weather events, which could improve emergency response and planning.
By Vandana Suresh, Texas A&M University College of Engineering March 2, 2020

A resident pulls personal belongings on a kayak as he wades through floodwaters
A Houston resident wades through a flooded road on Sept. 6, 2017. An algorithm developed by Texas A&M researchers can accurately predict the flow of floodwaters during weather events like hurricanes.

Getty Images


By incorporating the architecture of city drainage systems and readings from flood gauges into a comprehensive statistical framework, researchers at Texas A&M University can now accurately predict the evolution of floods in extreme situations like hurricanes. With their new approach, the researchers said their algorithm could forecast the flow of floodwater in almost real-time, which can then lead to more timely emergency response and planning.

“Not knowing where floodwater will flow next is particularly detrimental for first responders who need to gauge the level of flooding for their rescue operations,” said Ali Mostafavi, assistant professor in the Zachry Department of Civil and Environmental Engineering. “Our new algorithm considers the underground drainage channels to provide an accurate representation of how floods propagate. This tool, we think, can vastly help disaster management because first responders will be able to see which way floodwater will flow in real time.”

Hurricanes are notorious for wreaking havoc on shorelines, toppling trees, tearing down power lines and above all, causing severe floods. Conventionally, scientists have used physics-based models to predict where water might collect, overflow and cause flooding. In essence, these models capture how physical features of the earth’s surface and urban landscapes affect the flow of water over the ground.

While robust at predicting when and where floods will happen under most rainfall conditions, Mostafavi said these traditional models do not perform as well at predicting floods during incidents of torrential rainfall, like during Hurricane Harvey.

“Physics-based models offer one perspective on how floods can spread, which is extremely useful, but the picture they provide is somewhat incomplete,” he said. “We wanted to use existing data on how past floods have spread through the drainage channels to develop a model that would be able to predict, within a certain level of preciseness, how future floods will spread.”

graphic of a drainage sytsem
Blue circles denote nodes that have a small probability of flooding, while red circles show nodes that have a higher probability of inundation. The darker the red color, the higher chance the node has of flooding.

Courtesy of Ali Mostafavi

Drainage channels are an elaborate network of intertwined channels that meet together at junctions called nodes. Flooding in one channel can directly or indirectly affect other channels and cause floods to spread, much like a domino effect.

To predict which way floodwater will flow along drainage channels and cause an inundation, Mostafavi and his team developed a probability-based model that was fed the water-level readings on flood gauges for different time points during two major flooding events in Texas — Hurricane Harvey in 2017 and the Memorial Day flooding in 2015 in Houston.

Once the algorithm was trained on water flow patterns through the drainage networks for these heavy rainfall events, the researchers tested if their model worked by checking if it could predict the flood patterns that had been observed during Houston’s Tax Day flood in 2016.

This article by Vandana Suresh originally appeared on the College of Engineering website.

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