Texas A&M University researchers have developed a flooding prediction algorithm that could potentially aid disaster management.
The researchers said conventional flooding models do not perform well at predicting floods during incidents of torrential rainfall, such as during hurricanes and other extreme weather events.
Texas A&M's Ali Mostafavi and colleagues built a probability-based model that was fed water-level readings on flood gauges for different times during two major flooding events in Texas.
The researchers assessed the resulting algorithm by checking if it could predict the flood patterns observed during Houston's Tax Day flood in 2016; it was 85% accurate in predicting flood propagation through the drainage system for that incident.
Mostafavi said, "Traditional models and our data-driven models can be used to complement each other to give a more precise picture of where floodwater will go next."
From Texas A&M Today
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