HAZUS-MH Hurricane Model Methodology. I: Hurricane Hazard, Terrain, and Wind Load Modeling

Vickery, Peter J.; Lin, Jason; Skerlj, Peter F.; Twisdale, Lawrence A.; Huang, Kevin T.

doi:10.1061/(asce)1527-6988(2006)7:2(82)

Cited by 164 publications

(67 citation statements)

References 11 publications

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“…After several major hurricane and earthquake events worldwide, including Hurricane Hugo in 1989 and Hurricane Andrew in 1992, these firms made their models more sophisticated and their exposure databases more comprehensive (Cummins, 2007). Other companies subsequently developed in-house models, and US federal and state government supported development of two free open-source models: HAZUS-MH (Vickery et al, 2000a(Vickery et al, , 2000b(Vickery et al, , 2006a(Vickery et al, , 2006bFEMA, 2007) and the Florida Public Hurricane Loss Model (FPHLM) (Powell et al, 2005;Pinelli et al, 2008). The details and assumptions of the free models are available in the peerreview literature and technical documents, because they are intended for risk mitigation, regulation and emergency preparation, but the majority of cat models are proprietary and intended for pricing insurance or reinsurance policies, so their details are not public.…”

Section: Discussionmentioning

confidence: 99%

Risk and uncertainty in hydrometeorological hazards

Edwards¹,

Challenor²

2013

Risk and Uncertainty Assessment for Natural Hazards

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Section: Discussionmentioning

confidence: 99%

Risk and uncertainty in hydrometeorological hazards

Edwards¹,

Challenor²

2013

Risk and Uncertainty Assessment for Natural Hazards

View full text Add to dashboard Cite

“…The Federal Emergency Management Agency does have a GIS program, Hazard US, that can be used to run hypothetical scenarios of specified wind speed and rainfall at certain locations in order to predict overall loss (Schneider and Schauer, 2006;Vickery et al, 2006). Recently, there has been success in applying machine learning and ANNs to the areas of medicine and finance.…”

Section: Overview Of the Hurricane Impact Modelmentioning

confidence: 99%

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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“…Some of the key studies are briefly summarized. Vickery et al (2006a) and Vickery et al (2006b) described the overall algorithms based on different damage states, which are used in Hazus (HAZUS MHMR, 2003). Gurley et al (2005) developed the Florida Public Hurricane Loss Projection Model, which answered many questions on the subject of engineering-based vulnerability estimation.…”

Section: Vulnerability Estimationmentioning

confidence: 99%

Hurricane Loss Analysis Based on the Population-Weighted Index

Kakareko

Jung

Vanli

et al. 2017

Front. Built Environ.

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This paper discusses different measures for quantifying regional hurricane loss. The main measures used in the past are normalized percentage loss and dollar value loss. In this research, we show that these measures are useful but may not properly reflect the size of the population influenced by hurricanes. A new loss measure is proposed that reflects the hurricane impact on people occupying the structure. For demonstrating the differences among these metrics, regional loss analysis was conducted for Florida. The regional analysis was composed of three modules: the hazard module stochastically modeled the wind occurrence in the region; the vulnerability module utilized vulnerability functions developed in this research to calculate the loss; and the financial module quantified the hurricane loss. In the financial module, we calculated three loss metrics for certain region. The first metric is the average annual loss (AAL) which represents the expected loss per year in percentage. The second is the average annual dollar loss which represents the expected dollar amount loss per year. The third is the average annual population-weighted loss (AAPL)-a new measure proposed in this research. Compared with the AAL, the AAPL reflects the number of people influenced by the hurricane. The advantages of the AAPL are illustrated using three different analysis examples: (1) conventional regional loss analysis, (2) mitigation potential analysis, and (3) forecasted future loss analysis due to the change in population.

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HAZUS-MH Hurricane Model Methodology. I: Hurricane Hazard, Terrain, and Wind Load Modeling

Cited by 164 publications

References 11 publications

Risk and uncertainty in hydrometeorological hazards

Risk and uncertainty in hydrometeorological hazards

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

Hurricane Loss Analysis Based on the Population-Weighted Index

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