ArticleFrontiers in big data2022
Flood risk assessment for residences at the neighborhood scale by owner/occupant type and first-floor height.
Article in Frontiers in big data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Flood susceptibility assessment using three machine learning techniques and comparison of their performance.Scientific reports · 2026Article
- Theoretical Boundaries of Annual Flood Risk for Single-Family Homes Within the 100-Year Floodplain.International journal of environmental research · 2024Article
- Effects of damage initiation points of depth-damage function on flood risk assessment.npj natural hazards · 2024Article
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Authors and funding
5 authors.
Funding
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Abstract
Evaluating flood risk is an essential component of understanding and increasing community resilience. A robust approach for quantifying flood risk in terms of average annual loss (AAL) in dollars across multiple homes is needed to provide valuable information for stakeholder decision-making. This research develops a computational framework to evaluate AAL at the neighborhood level by owner/occupant type (i.e., homeowner, landlord, and tenant) for increasing first-floor height (FFH). The AAL values were calculated here by numerically integrating loss-exceedance probability distributions to represent economic annual flood risk to the building, contents, and use. A simple case study for a census block in Jefferson Parish, Louisiana, revealed that homeowners bear a mean AAL of $4,390 at the 100-year flood elevation (
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