Investigating Uncertainty in Developing Regional Building Inventories for Flood Damage Prediction

Gutenson, Joseph L.; Oubeidillah, Abdoul; Ernest, Andrew; Zhu, Lingkai; Zhang, X.; Sadeghi, Saeideh

doi:10.1061/(asce)nh.1527-6996.0000240

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…For example, Location 13 in Table 7, a single story home with no basement incurs $5,437 in structural damage in HEC-FIA, $12,381 in damage within Hazus-MH, and $2,525 and $3,787 in Flood Damage Wizard. This result supports the previous conclusions of Gutenson et al (2016) which demonstrate significant uncertainty in the usage of depth-damage functions, even when the assigned depth-damage functions are similar. In general, both FIA and Hazus-MH offer larger estimates of structural damage, while Flood Damage Wizard estimates larger sums of content damage.…”

Section: Combining Address Point and Cadastre Datasetssupporting

confidence: 91%

“…This result supports the previous conclusions of Gutenson et al . () which demonstrate significant uncertainty in the usage of depth‐damage functions, even when the assigned depth‐damage functions are similar. In general, both FIA and Hazus‐MH offer larger estimates of structural damage, while Flood Damage Wizard estimates larger sums of content damage.…”

Section: Discussionmentioning

confidence: 99%

“…Among the three models in this study, only HEC-FIA and Flood Damage Wizard are capable of approximating uncertainty explicitly. The damage distribution in Flood Damage Wizard is currently an equally weighted binomial distribution, where the authors consider minimum damage to a building to be the least valuable component of the building (either structure, contents, or inventory) and maximum damage to be the scenario in which all two/three components of a building (structure, contents, and/or inventory) accrue damage (Gutenson et al, 2016). The authors summarize this distribution of damage in two ways.…”

Section: Methods Of Comparisonmentioning

confidence: 99%

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Rapid Flood Damage Prediction and Forecasting Using Public Domain Cadastral and Address Point Data with Fuzzy Logic Algorithms

Gutenson

Ernest

Oubeidillah

et al. 2017

J American Water Resour Assoc

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National Flood Interoperability Experiment (NFIE) derived technologies and workflows will offer the ability to rapidly forecast flood damages. Address Points used by emergency management personnel approximate the locations of buildings, and they are a common operating picture for emergency responders. Most United States (U.S.) county tax assessment offices throughout the contiguous U.S. (CONUS) produce georeferenced cadastral data. To varying degrees, these parcel data describe building characteristics of structures within the parcel. Address Point data with cadastral data offers the ability to rapidly develop building inventories for flood damage estimation. Flood damage forecasts can expedite recovery and improve short-term flood resilience. In this work the authors evaluate Flood Damage Wizard, a proposed open source platform independent methodology. Flood Damage Wizard uses point shapefile building information to estimate flood damage to buildings by finding the appropriate depth-damage function using fuzzy-text matching. The authors apply Flood Damage Wizard using Address Point and parcel datasets to demonstrate a method of estimating flood damage to buildings nearly anywhere within the CONUS. Results indicate using Address Point and cadastral datasets can generate total flood damage estimates approximate to those estimated using existing software solutions Hazus-MH and HEC-FIA with minimal manual processing of input data.(KEY TERMS: risk assessment; planning; flooding; geospatial analysis; damage assessment; open source software; rapid damage assessment.)

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Section: Combining Address Point and Cadastre Datasetssupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Methods Of Comparisonmentioning

confidence: 99%

See 1 more Smart Citation

Rapid Flood Damage Prediction and Forecasting Using Public Domain Cadastral and Address Point Data with Fuzzy Logic Algorithms

Gutenson

Ernest

Oubeidillah

et al. 2017

J American Water Resour Assoc

Self Cite

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Predicting Flash Flood Economic Damage at the Community Scale: Empirical Zero-Inflated Model with Semicontinuous Data

et al. 2023

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A Socioeconomic Dataset of the Risk Associated with the 1% and 0.2% Return Period Stillwater Flood Elevation under Sea-Level Rise for the Northern Gulf of Mexico

Angel

Yoskowitz

Bilskie

et al. 2022

Data

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Storm surge flooding can cause significant damage to coastal communities. In addition, coastal communities face an increased risk of coastal hazards due to sea-level rise (SLR). This research developed a dataset to communicate the socioeconomic consequences of flooding within the 1% and 0.2% Annual Exceedance Probability Floodplain (AEP) under four SLR scenarios for the Northern Gulf of Mexico region. Assessment methods primarily used HAZUS-MH software, a GIS-based modeling tool developed by the Federal Emergency Management Agency in the United States, to estimate natural disasters’ physical, economic, and social impacts. This dataset consists of 29 shapefiles containing seven different measures of storm surge inundation impacts under SLR (including building damage, displaced people and shelter needs, road exposure, essential facilities, wastewater treatment plants, bridges, and vehicle damage). The data is publicly available under the Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC).

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Investigating Uncertainty in Developing Regional Building Inventories for Flood Damage Prediction

Cited by 3 publications

References 21 publications

Rapid Flood Damage Prediction and Forecasting Using Public Domain Cadastral and Address Point Data with Fuzzy Logic Algorithms

Rapid Flood Damage Prediction and Forecasting Using Public Domain Cadastral and Address Point Data with Fuzzy Logic Algorithms

Predicting Flash Flood Economic Damage at the Community Scale: Empirical Zero-Inflated Model with Semicontinuous Data

A Socioeconomic Dataset of the Risk Associated with the 1% and 0.2% Return Period Stillwater Flood Elevation under Sea-Level Rise for the Northern Gulf of Mexico

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