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<p>First Floor Elevation (FFE) is a crucial indicator used in the United States for assessing the vulnerability of buildings to flood events. However, most buildings within floodplains lack accurate FFE data. On-site FFE data collection could be costly and time-consuming. To facilitate efficient FFE data collection, this paper explores the extent to which FFEs for an entire community’s building stock can be estimated using limited FFE data records. More specifically, this study leverages geostatistical imputation techniques to fill in missing FFE data. The proposed approach first stratifies buildings based on attributes such as foundation type and then applies Kriging to estimate missing FFEs by leveraging spatial relationships and distances between known data points. To demonstrate the applicability and validity, the approach is tested in three New Jersey townships: the inland township Manville and the two coastal townships Longport and Ventnor City. By transforming data into First Floor Height and stratifying by building type, the methodology achieved a Root Mean Square Error (RMSE) as low as 0.9893 ft in Manville, with variations in coastal towns due to unique structural and geographic factors. The findings highlight the potential of combining geostatistical modeling with building-specific attributes to enhance flood vulnerability assessments. This approach not only addresses critical data deficiencies but also supports informed decision-making in resilience planning and natural hazard mitigation.</p>

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A geostatistical imputation of first floor elevation data for mapping flood vulnerability

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月6日

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  • Prarthana Raja,
  • Yitong Li,
  • Jie Gong

摘要

First Floor Elevation (FFE) is a crucial indicator used in the United States for assessing the vulnerability of buildings to flood events. However, most buildings within floodplains lack accurate FFE data. On-site FFE data collection could be costly and time-consuming. To facilitate efficient FFE data collection, this paper explores the extent to which FFEs for an entire community’s building stock can be estimated using limited FFE data records. More specifically, this study leverages geostatistical imputation techniques to fill in missing FFE data. The proposed approach first stratifies buildings based on attributes such as foundation type and then applies Kriging to estimate missing FFEs by leveraging spatial relationships and distances between known data points. To demonstrate the applicability and validity, the approach is tested in three New Jersey townships: the inland township Manville and the two coastal townships Longport and Ventnor City. By transforming data into First Floor Height and stratifying by building type, the methodology achieved a Root Mean Square Error (RMSE) as low as 0.9893 ft in Manville, with variations in coastal towns due to unique structural and geographic factors. The findings highlight the potential of combining geostatistical modeling with building-specific attributes to enhance flood vulnerability assessments. This approach not only addresses critical data deficiencies but also supports informed decision-making in resilience planning and natural hazard mitigation.