Inspired by epidemiology, team develops model to predict flooding

Shutterstock.com / Paul Kulinich

Inspired by the same modeling and mathematical laws used to predict the spread of pandemics, researchers at Texas A&M University have created a model to accurately forecast the spread and recession process of floodwaters in urban road networks. With this new approach, researchers have created a simple and powerful mathematical approach to a complex problem.

“We were inspired by the fact that the spread of epidemics and pandemics in communities has been studied by people in health sciences and epidemiology and other fields, and they have identified some principles and rules that govern the spread process in complex social networks,” said Ali Mostafavi, associate professor in the Zachry Department of Civil & Environmental Engineering. “So we ask ourselves, are these spreading processes the same for the spread of flooding in cities? We tested that, and surprisingly, we found that the answer is yes.”

The findings of this study were recently published in Nature Scientific Reports.

The contagion model, Susceptible-Exposed-Infected-Recovered (SEIR), is used to mathematically model the spread of infectious diseases. In relation to flooding, Mostafavi and his team integrated the SEIR model with the network spread process in which the probability of flooding of a road segment depends on the degree to which the nearby road segments are flooded.

In the context of flooding, susceptible is a road that can be flooded because it is in a flood plain; exposed is a road that has flooding due to rainwater or overflow from a nearby channel; infected is a road that is flooded and cannot be used; and recovered is a road where the floodwater has receded.

The research team verified the model’s use with high-resolution historical data of road flooding in Harris County during Hurricane Harvey in 2017. The results show that the model can monitor and predict the evolution of flooded roads over time.

“The power of this approach is it offers a simple and powerful mathematical approach and provides great potential to support emergency managers, public officials, residents, first responders and other decision makers for flood forecast in road networks,” Mostafavi said.

This research is funded by National Science Foundation’s CRISP 2.0 Type 2 project in which Mostafavi is the lead principal investigato

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