Beyond the Floodplain: A Multi-Criteria Framework for Emergency Shelter Placement in Buncombe County, NC

The catastrophic impact of Hurricane Helene proved that standard FEMA flood maps are often inadequate for assessing risk in complex mountainous terrain. Using Buncombe County, North Carolina, as a case study, this research introduces a replicable framework for siting emergency shelters based on a multi-dimensional Flood Risk Index. By synthesizing HAND-derived inundation data, land-use intensity, and a machine learning-based Socio-Economic Vulnerability Index (SEVI), we mapped the intersection of hazard and vulnerability. Our analysis reveals a significant misalignment—a large portion of the current shelter network sits in high-risk zones, while safer upland corridors in the north and west remain underutilized. This study delivers a data-driven roadmap for disaster preparedness, ensuring that future shelter placement is not only safe from terrain-driven floods but also strategically and equitably located.

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Publication Details

Journal
Applied Sciences
Published
2026-03-09
DOI
https://doi.org/10.3390/app16052608
Primary Topic
Flood Risk Assessment and Management
Type
article
Field-Weighted Citation Impact
0.00
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article

Beyond the Floodplain: A Multi-Criteria Framework for Emergency Shelter Placement in Buncombe County, NC

Kibri Hutchison Everett, Shen-En Chen, Srijana Raut, Tung Le et al.
Applied Sciences
Flood Risk Assessment and Management
article

Beyond the Floodplain: A Multi-Criteria Framework for Emergency Shelter Placement in Buncombe County, NC

Kibri Hutchison Everett, Shen-En Chen, Srijana Raut, Tung Le, Jay Wu, Sodiq M. Balogun
article en

Abstract

The catastrophic impact of Hurricane Helene proved that standard FEMA flood maps are often inadequate for assessing risk in complex mountainous terrain. Using Buncombe County, North Carolina, as a case study, this research introduces a replicable framework for siting emergency shelters based on a multi-dimensional Flood Risk Index. By synthesizing HAND-derived inundation data, land-use intensity, and a machine learning-based Socio-Economic Vulnerability Index (SEVI), we mapped the intersection of hazard and vulnerability. Our analysis reveals a significant misalignment—a large portion of the current shelter network sits in high-risk zones, while safer upland corridors in the north and west remain underutilized. This study delivers a data-driven roadmap for disaster preparedness, ensuring that future shelter placement is not only safe from terrain-driven floods but also strategically and equitably located.

Applied SciencesVol. 16(5)
University of North Carolina at Charlotte (US)
Climate action
Openalex Percentile: Top 42%
Flood Risk Assessment and Management
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