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A geostatistical imputation of first floor elevation data for mapping flood vulnerability
Prarthana Raja1, Yitong Li2, Jie Gong1
1Department of Civil and Environmental Engineering, Rutgers University, Piscataway, NJ 08854 USA.
Natural Hazards (Dordrecht, Netherlands)
|April 9, 2026
Summary
This study estimates missing First Floor Elevation (FFE) data for buildings using geostatistical methods. This approach improves flood vulnerability assessments and supports resilience planning.
Area of Science:
- Geosciences
- Urban Planning
- Data Science
Background:
- Accurate First Floor Elevation (FFE) data is vital for flood vulnerability assessment in the US.
- Most buildings in floodplains lack essential FFE data, hindering effective risk management.
- Manual FFE data collection is often prohibitively expensive and time-consuming.
Purpose of the Study:
- To explore the estimation of community-wide building First Floor Elevations (FFEs) using limited available data.
- To develop and validate a geostatistical imputation technique for filling missing FFE records.
- To enhance flood vulnerability assessments and inform resilience planning through improved data.
Main Methods:
- Stratified buildings by attributes like foundation type.
- Applied Kriging, a geostatistical interpolation technique, to estimate missing FFEs.
- Leveraged spatial relationships and distances between known FFE data points.
Main Results:
- Achieved a low Root Mean Square Error (RMSE) of 0.9893 ft in Manville.
- Demonstrated applicability in diverse settings, including inland and coastal New Jersey townships.
- Observed variations in coastal areas due to unique structural and geographic characteristics.
Conclusions:
- Geostatistical modeling combined with building attributes effectively estimates missing FFEs.
- The approach addresses critical data gaps for flood vulnerability assessments.
- Findings support informed decision-making for natural hazard mitigation and resilience strategies.
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