Using artificial neural networks to forecast economic impact of multi-hazard hurricane-based events

Pilkington, Stephanie; Mahmoud, Hussam

doi:10.1080/23789689.2016.1179529

Cited by 20 publications

(21 citation statements)

References 13 publications

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“…The use of four potential landfall locations assists in illustrating storm track, since storms such as 2011 Hurricane Irene technically landfall more than once as it travels up the East Coast. The hidden layer serves as additional nodal connections to transfer and relate the inputs to each other and the resulting output: the IL (Pilkington and Mahmoud, 2016). Each hurricane season, the model networks are built (trained, validated, and tested) using all land-falling historical events since 1998 (data are not consistent prior to this date).…”

Section: Overview Of the Hurricane Impact Modelmentioning

confidence: 99%

“…Each hurricane season, the model networks are built (trained, validated, and tested) using all land-falling historical events since 1998 (data are not consistent prior to this date). The first model built, as discussed in Pilkington and Mahmoud (2016), used one network of the lowest possible percent error. There may be more than one network with such a low error but it may not produce the same output results or confidence in those results.…”

Section: Overview Of the Hurricane Impact Modelmentioning

confidence: 99%

“…Ideally, the goal would be to tie all variables together and communicate their effect in terms the public can easily understand: causalities and/or cost. In line with this goal, a recent Hurricane Impact Level (HIL) Ranking System, shown in Table 2, was developed by Pilkington and Mahmoud (2016) to correlate total economic damage to an impact level (IL). The economic damage referenced in the HIL Ranking System is based on National Hurricane Center (NHC) reporting, which account for all infrastructure damage and insurance payouts for things such as temporary housing when a home is uninhabitable.…”

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confidence: 99%

“…This article addresses whether using an ANN model developed by Pilkington and Mahmoud (2016), can be applied real-time as an accurate forecasting tool to communicate the overall impending impact of a land-falling hurricane within the US. The economic damage is surveyed and tallied after the storm passes, insurance claims are filed, and federal funding is distributed in response.…”

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confidence: 99%

“…TaBle 2 | Hurricane Impact Level ranking system (Pilkington and Mahmoud, 2016). The majority of current studies assess the wind hazard of tropical cyclones due to the main focus within building codes in tropical cyclone prone areas, at least in the US.…”

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confidence: 99%

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Real-time Application of the Multihazard Hurricane Impact Level Model for the Atlantic Basin

Pilkington

Mahmoud

2017

Front. Built Environ.

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Tropical cyclones are an example of a multihazard event with impacts that can highly vary depending on landfall location, wind speed, storm surge, and inland flooding from precipitation. These storms are typically categorized by their wind speed and pressure, while evacuation orders are typically given based on storm surge. The general public relies on these single hazard assessment parameters when attempting to understand the risk of an oncoming event. However, after the fact, these events are ranked by economic damage and death toll. Therefore, it is imperative that when these events are communicated to the public, during the forecast period, the multiple hazards are incorporated in terms the public can easily associate with, such as economic damage. This article provides an evaluation of the potential for real-time use of artificial neural networks, through the utilization of an already developed Hurricane Impact Level (HIL) Model, to forecast a range of economic damage from tropical cyclone events, during the 2015 and 2016 United States hurricane season. The HIL Model is built prior to the start of each season and simulated every 3 h, in conjunction with National Hurricane Center (NHC) issued advisories, for oncoming tropical cyclones forecasted to make landfall. Weaker and more common tropical cyclones have a less varied forecast and produce more accurate impact level (IL) predictions. More complicated and uncertain events, such as 2016 Hurricane Matthew, require the user's discretion in communicating varying landfall locations for a complex track forecast to the model. As NHC forecasts change with respect to both track and meteorological hazards affecting land, the estimated IL and the HIL model confidence will also change. In other words, if a track shifts to a more vulnerable location, or to more locations, or the meteorological hazards increase, the IL will subsequently increase. All tropical cyclones from the 2015 and 2016 seasons demonstrate the validity of the HIL Model with a forecast confidence of at least 60% for up to 30 h out from an impending landfall.

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Section: Overview Of the Hurricane Impact Modelmentioning

confidence: 99%

Section: Overview Of the Hurricane Impact Modelmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Real-time Application of the Multihazard Hurricane Impact Level Model for the Atlantic Basin

Pilkington

Mahmoud

2017

Front. Built Environ.

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North Atlantic tropical cyclone size and storm surge reconstructions from 1950-present

Gori

Lin

Schenkel

et al. 2022

Preprint

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Tropical cyclones (TCs) are one of the largest threats to coastal communities worldwide (Dullaart et al., 2021), and are the costliest natural hazard impacting the United States (Smith & Katz, 2013). Landfalling TCs can bring extreme wind, storm surge, and rainfall to coastal regions, resulting in widespread damage and loss of life. For example, the Galveston hurricane of 1900 caused at least 6,000 fatalities, and remains the deadliest US hurricane to date (Cline, 1900). More recently, Hurricanes Katrina (2005), Sandy (2012), and Harvey (2017) caused extreme flooding due to their rainfall and storm surge with total damages ranging from $80 to $150 billion (2022 USD) for each of the storms (Blake & Zelinsky, 2017;Blake et al., 2013;Knabb et al., 2005). Given the magnitude and frequency of TC-induced catastrophes, it is vital to understand and characterize the wind, rain and surge hazards from historical hurricanes. Developing spatially and temporally continuous records of TC storm characteristics and associated hazards can aid in risk assessment, emergency planning, and mitigation efforts. TC wind, rainfall and surge severity in coastal regions depends on storm characteristics including intensity (maximum sustained wind speed-V max and minimum central pressure-P min ), inner size (i.e., radius to maximum wind-R max ), translation speed, and approach angle to the coast (Irish et al., 2008;Ramos-Valle et al., 2020;Thomas et al., 2019). Peak storm surges also vary based on geographic characteristics, such as coastline shape

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Risk and Reliability Analysis

Gardoni

2017

Springer Series in Reliability Engineering

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Using artificial neural networks to forecast economic impact of multi-hazard hurricane-based events

Cited by 20 publications

References 13 publications

Real-time Application of the Multihazard Hurricane Impact Level Model for the Atlantic Basin

Real-time Application of the Multihazard Hurricane Impact Level Model for the Atlantic Basin

North Atlantic tropical cyclone size and storm surge reconstructions from 1950-present

Risk and Reliability Analysis

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