Contagion model predicts flooding in urban areas
The model can accurately forecast the spread and recession process of floodwaters in urban road networks

Date:
August 24, 2020
Source:
Texas A&M University
Summary:
Inspired by the same modeling and mathematical laws used to predict
the spread of pandemics, researchers 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.



FULL STORY ========================================================================== 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
Dr. Ali Mostafavi, associate professor in the Zachry Department of Civil
and 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.

The proposed model can achieve decent precision and recall for the
spatial spread of the flooded roads.

"If you look at the flood monitoring system of Harris County, it can
show you if a channel is overflowing now, but they're not able to
predict anything about the next four hours or next eight hours. Also,
the existing flood monitoring systems provide limited information about
the propagation of flooding in road networks and the impacts on urban
mobility. But our models, and this specific model for the road networks,
is robust at predicting the future spread of flooding," he said. "In
addition to flood prediction in urban networks, the findings of this
study provide very important insights about the universality of the
network spread processes across various social, natural, physical and engineered systems; this is significant for better modeling and managing cities, as complex systems." The only limitation to this flood prediction model is that it cannot identify where the initial flooding will begin,
but Mostafavi said there are other mechanisms in place such as sensors
on flood gauges that can address this.

"As soon as flooding is reported in these areas, we can use our model,
which is very simple compared to hydraulic and hydrologic models, to
predict the flood propagation in future hours. The forecast of road
inundations and mobility disruptions is critical to inform residents to
avoid high-risk roadways and to enable emergency managers and responders
to optimize relief and rescue in impacted areas based on predicted
information about road access and mobility.

This forecast could be the difference between life and death during
crisis response," he said.

Civil engineering doctoral student and graduate research assistant
Chao Fan led the analysis and modeling of the Hurricane Harvey data,
along with Xiangqi (Alex) Jiang, a graduate student in computer science,
who works in Mostafavi's UrbanResilience.AI Lab.

"By doing this research, I realize the power of mathematical models in addressing engineering problems and real-world challenges.

This research expands my research capabilities and will have a long-term
impact on my career," Fan said. "In addition, I am also very excited
that my research can contribute to reducing the negative impacts of
natural disasters on infrastructure services."

========================================================================== Story Source: Materials provided by Texas_A&M_University. Original
written by Alyson Chapman.

Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Chao Fan, Xiangqi Jiang, Ali Mostafavi. A network percolation-based
contagion model of flood propagation and recession in
urban road networks. Scientific Reports, 2020; 10 (1) DOI:
10.1038/s41598-020-70524- x ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/08/200824144410.htm

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