An integrated web framework for HAZUS-MH flood loss estimation analysis

2019 ◽  
Vol 99 (1) ◽  
pp. 275-286 ◽  
Author(s):  
Enes Yildirim ◽  
Ibrahim Demir
Keyword(s):  
Loss Estimation ◽  
Web Framework ◽  
Flood Loss

HAZUS-MH Flood Loss Estimation Methodology. I: Overview and Flood Hazard Characterization

2006 ◽  
Vol 7 (2) ◽  
pp. 60-71 ◽  
Author(s):  
Charles Scawthorn ◽  
Neil Blais ◽  
Hope Seligson ◽  
Eric Tate ◽  
Edward Mifflin ◽  
...  
Keyword(s):  
Flood Hazard ◽  
Loss Estimation ◽  
Hazard Characterization ◽  
Flood Loss

scholarly journals Application and validation of FLEMOcs – a flood-loss estimation model for the commercial sector

2010 ◽  
Vol 55 (8) ◽  
pp. 1315-1324 ◽  
Author(s):  
Isabel Seifert ◽  
Heidi Kreibich ◽  
Bruno Merz ◽  
Annegret H. Thieken
Keyword(s):  
Loss Estimation ◽  
Commercial Sector ◽  
Estimation Model ◽  
Flood Loss

scholarly journals Comparison of Neighborhood-Scale, Residential Property Flood-Loss Assessment Methodologies

2021 ◽  
Vol 9 ◽  
Author(s):  
Rubayet Bin Mostafiz ◽  
Carol J. Friedland ◽  
Md Asif Rahman ◽  
Robert V. Rohli ◽  
Eric Tate ◽  
...  
Keyword(s):  
Loss Estimation ◽  
Return Periods ◽  
Data Set ◽  
Building Inventory ◽  
Significant Difference ◽  
Flood Loss ◽  
Neighborhood Scale ◽  
The Individual ◽  
Building Loss ◽  
Level 2

Leading flood loss estimation models include Federal Emergency Management Agency’s (FEMA’s) Hazus, FEMA’s Flood Assessment Structure Tool (FAST), and (U.S.) Hydrologic Engineering Center’s Flood Impact Analysis (HEC-FIA), with each requiring different data input. No research to date has compared the resulting outcomes from such models at a neighborhood scale. This research examines the building and content loss estimates by Hazus Level 2, FAST, and HEC-FIA, over a levee-protected census block in Metairie, in Jefferson Parish, Louisiana. Building attribute data in National Structure Inventory (NSI) 2.0 are compared against “best available data” (BAD) collected at the individual building scale from Google Street View, Jefferson Parish building inventory, and 2019 National Building Cost Manual, to assess the sensitivity of input building inventory selection. Results suggest that use of BAD likely enhances flood loss estimation accuracy over existing reliance on default data in the software or from a national data set that generalizes over a broad scale. Although the three models give similar mean (median) building and content loss, Hazus Level 2 results diverge from those produced by FAST and HEC-FIA at the individual building level. A statistically significant difference in mean (median) building loss exists, but no significant difference is found in mean (median) content loss, between building inventory input (i.e., NSI 2.0 vs BAD), but both the building and content loss vary at the individual building scale due to difference in building-inventory-reported foundation height, foundation type, number of stories, replacement cost, and content cost. Moreover, building loss estimation also differs significantly by depth-damage function (DDF), for flood depths corresponding with the longest return periods, with content loss differing significantly by DDF at all return periods tested, from 10 to 500 years. Knowledge of the extent of estimated differences aids in understanding the degree of uncertainty in flood loss estimation. Much like the real estate industry uses comparable home values to appraise a home, flood loss planners should use multiple models to estimate flood-related losses. Moreover, results from this study can be used as a baseline for assessing losses from other hazards, thereby enhancing protection of human life and property.

scholarly journals Multivariate Flood Loss Estimation of the 2018 Bago Flood in Myanmar

2020 ◽  
Vol 15 (3) ◽  
pp. 300-311 ◽  
Author(s):  
Win Win Zin ◽  
Akiyuki Kawasaki ◽  
Georg Hörmann ◽  
Ralph Allen Acierto ◽  
Zin Mar Lar Tin San ◽  
...  
Keyword(s):  
Water Depth ◽  
Building Material ◽  
Damage Assessment ◽  
Flood Damage ◽  
Loss Estimation ◽  
Flood Hazards ◽  
Estimation Model ◽  
Flood Duration ◽  
Flood Loss ◽  
Flood Damage Assessment

Flood loss models are essential tools for assessing flood risk. Flood damage assessment provides decision makers with critical information to manage flood hazards. This paper presents a multivariable flood damage assessment based on data from residential building and content damage from the Bago flood event of July 2018. This study aims to identify the influences on building and content losses. We developed a regression-based flood loss estimation model, which incorporates factors such as water depth, flood duration, building material, building age, building condition, number of stories, and floor level. Regression approaches, such as stepwise and best subset regression, were used to create the flood damage model. The selection was based on Akaike’s information criterion (AIC). We found that water depth, flood duration, and building material were the most significant factors determining flood damage in the residential sector.

scholarly journals Flood loss estimation using 3D city models and remote sensing data

2018 ◽  
Vol 105 ◽  
pp. 118-131 ◽  
Author(s):  
Kai Schröter ◽  
Stefan Lüdtke ◽  
Richard Redweik ◽  
Jessica Meier ◽  
Mathias Bochow ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Loss Estimation ◽  
3D City Models ◽  
Sensing Data ◽  
Flood Loss ◽  
City Models

Developing an agricultural flood loss estimation function (case study: rice)

2012 ◽  
Vol 64 (1) ◽  
pp. 405-419 ◽  
Author(s):  
Zahra Ganji ◽  
Alireza Shokoohi ◽  
Jamal M. V. Samani
Keyword(s):  
Loss Estimation ◽  
Flood Loss ◽  
Estimation Function

Selection of hazard-consistent hurricane scenarios for regional combined hurricane wind and flood loss estimation

2017 ◽  
Vol 91 (2) ◽  
pp. 671-696
Author(s):  
Bin Pei ◽  
Weichiang Pang ◽  
Firat Y. Testik ◽  
Nadarajah Ravichandran ◽  
Fangqian Liu
Keyword(s):  
Loss Estimation ◽  
Hurricane Wind ◽  
Flood Loss ◽  
Selection Of

scholarly journals Study of Flood Loss Estimation using Hazus-MH 2.1

2013 ◽  
Vol 24 (1) ◽  
pp. 29-58 ◽  
Author(s):  
유순영 ◽  
안현욱
Keyword(s):  
Loss Estimation ◽  
Flood Loss
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