Abstract

The United States coastline, where over 50% the population lives, is vulnerable to hurricanes along the East and Gulf coasts. However, the question remains as to whether these two areas are equally resilient to a landfalling hurricane. In addition, while it is assumed that improvements in building codes, infrastructure protections, and changing policy over the past century have been effective in reducing the impacts to a community from historically extreme hurricane events, such an assumption is still to be validated. Here, a multi-hazard artificial neural network model is used to address these questions. The Hurricane Impact Level Model is the first prediction model to utilize machine-learning techniques (artificial neural networks) to established complex connections between all meteorological factors (wind, pressure, storm surge, and precipitation resulting in inland flooding) of a tropical cyclone and how those interact with the location of landfall to produce a certain level of economic damage. This model allows for a more all-encompassing assessment of how the impacts of tropical cyclones vary along the coastline. The Hurricane Impact Level Model was trained with historical tropical cyclone events from 1998 to present day, resulting in established locational associations to modern relevant building codes and mitigation practices. Simulating the meteorological factors from historical events allows for a new assessment of economic impact changes due to infrastructure improvements and policy adaptations over time. In essence, if Hurricane Sandy hit Florida instead of New York, it would have a lower economic impact due to lower population density and more stringent building codes, which the artificial neural network has associated with the latitudes and longitudes within the state of Florida. If the Galveston hurricane were to hit today, the seawall would not succeed in lowering the economic impact to the Texas coastline. Over the years, significant effort has been put in to improving the resiliency of the United States coastline, mainly in the southern states, but it has not been enough to counteract the effects of population growth within coastal counties.

Highlights

  • The United States coastline, where over 50% the population lives, is vulnerable to hurricanes along the East and Gulf coasts

  • Since risk mitigation practices have become more common in hazard vulnerable areas, the topic of disasters has begun to shift from vulnerability and risk to the concept of resilience, to which mitigation practices can improve (Mileti, 1999; Godschalk, 2003; Bruneau et al, 2003; Burby et al, 2000)

  • Resilience is often evaluated at the community level, such as with the Disaster Resilience of Place Model, which begins with the “antecedent” conditions, or the locational characteristics (Cutter et al, 2008)

Read more

Summary

Vulnerability of coastal communities

Today more than 50% of the United States population currently resides in coastal counties, vulnerable to earthquakes on the West Coast and hurricanes on the Gulf and East Coast (Crossett et al, 2004). Natural, and social systems that are specific to one place over a (long) period of time, and could be viewed as a network of physical and human systems (Godschalk, 2003) These systems do not change quickly; the infrastructure and societal awareness of the Galveston, Texas region, for example, in 1900 would be inherently different than today; in the late 1990s/early 2000s, the overall infrastructure and awareness would not vary significantly. In keeping with this concept, the locational characteristics must be considered as interactive with hazard characteristics during an event in order to anticipate the resulting outcome or impact (Cutter et al, 2008; Pilkington and Mahmoud, 2016). Advisories for storm events such as Hurricane Katrina and Sandy

Example event
Discussion
Findings
Additional information
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call