{"id":1455,"date":"2020-08-28T08:01:50","date_gmt":"2020-08-28T08:01:50","guid":{"rendered":"http:\/\/thepoetryofscience.peachpuff-wolverine-566518.hostingersite.com\/?p=1455"},"modified":"2020-08-28T08:01:50","modified_gmt":"2020-08-28T08:01:50","slug":"an-outbreak-of-flooding","status":"publish","type":"post","link":"https:\/\/scienceblog.com\/thepoetryofscience\/1455\/an-outbreak-of-flooding\/","title":{"rendered":"An Outbreak of Flooding"},"content":{"rendered":"

Struggling beneath the weight
\nof accumulation you buckle
\nin the rains;
\na surplus of backwash
\nbaptising you with the
\nfilthy discharge of a
\nthousand vanquished drains.<\/p>\n

Every street and boulevard
\nfades beneath
\nthe waves;
\nthoroughfares gasp for breath,
\nthe city\u2019s arteries washed
\nclean until they lead to
\nshallow graves.<\/p>\n

Inundated in the
\nuncertainty with which
\nyou subside;
\nyour basic functions
\nreduced to nothing
\nmore than treading water
\nagainst the rising tide.<\/p>\n

Regretfully we chart
\nthe spread down
\nevery path and drive;
\ntracing geometric channels
\nthrough the outburst
\nso that others
\nmight survive.<\/p>\n

\"\"<\/a>
Flooding in Port Arthur, Texas on 31st August 2017, during Hurricane Harvey (Image Credit: SC National Guard \/ Public Domain).<\/figcaption><\/figure>\n

This poem is inspired by recent research<\/a>, which has found that flooding can be predicted in urban areas by adapting the modelling and mathematical laws that are used to predict the spread of pandemics.<\/p>\n

Inspired by the same processes that are used to predict the spread of pandemics, researchers have created a model to accurately forecast the spread of floodwaters in urban road networks. The Susceptible-Exposed-Infected-Recovered (or SEIR) model describes the spreading of an epidemic in which a susceptible individual becomes spontaneously immune or recovered after being infected by a contagious disease. The SEIR model was adapted to describe how the probability of the flooding of a road segment depends on the degree to which other nearby road segments are flooded. Here \u2018susceptible\u2019 refers to a road that can be flooded because it is in a flood plain, \u2018exposed\u2019 is a road that has flooding due to rainwater or overflow from a nearby channel, \u2018infected\u2019 is a road that is flooded and cannot be used, and \u2018recovered\u2019 is a road where the floodwater has receded.<\/p>\n

The accuracy of this model was verified using historical data of road flooding in Harris County, Texas during Hurricane Harvey in 2017, where it was found that the model was able to predict road flooding events over this region and time period with approximately 90% accuracy. The accuracy of this model would suggest that this approach will be of great benefit to emergency managers, public officials, citizens, first responders, and other decision-makers during major flooding events.<\/p>\n