Abstract
Purpose: Statistical data imputation methods are important in a wide range of scientific research; however, in construction management research they are not used widely. Specifically, in research for building loss studies due to extreme hazard events, data are frequently missing, inaccessible, spurious, or expensive to collect. First-floor elevation (FFE) data are vital in building flood loss analysis, so the lack of high-quality FFE data before and/or after elevating structures represents a major barrier to understanding avoided loss in flood mitigation projects. While a few guidelines exist to estimate FFE, the guidelines lack information on estimation of FFE for mitigated and non-mitigated buildings. Existing techniques tend to rely on recommendations by professional engineers which have not been evaluated for statistical fit in elevated homes in Louisiana, U.S.A. Methods: This Louisiana-based case study evaluates statistically the effectiveness of existing guidelines on building elevation data. Furthermore, it provides a state-of-art methodology to impute missing FFE data statistically for buildings in mitigated and unmitigated conditions without relying on building foundation type data, which itself is commonly needed but often missing in previous building mitigation avoided loss studies. Findings: Results here suggest that existing guidelines for FFE estimation match reported FFE only moderately well in flood-mitigated residences in Louisiana. Moreover, an update and inclusion of foundation type data in the guidelines would improve FFE estimates for Louisiana homes. Among the imputation methods by multiple linear regression, random forest, and generalized additive models (GAM) overlay, the GAM model performs most effectively based on the accuracy in data imputation for missing FFE data. These results will assist builders, developers, and communities in their quest to enhance resilience to the ever-increasing flood hazard.
Highlights
BackgroundHazard mitigation is any intentional action that decreases loss from natural disaster events by reducing vulnerability
This research contributes to the development of knowledge in the field of flood loss analysis by providing statistical imputation methods to find missing FFE0 and FFE1 data
The statistical t-test shows that the results of estimation methods in the Federal Emergency Management Agency (FEMA) guidelines do not differ significantly from Louisiana Hazard Mitigation (LAHM) observations in this study
Summary
Hazard mitigation is any intentional action that decreases loss from natural disaster events by reducing vulnerability. Flood loss functions are used to calculate the flood loss in buildings These functions, often referred to as depth–damage curves (e.g., Gulf Engineers & Consultants [GEC], 2006; FEMA, 2015), are based on a single independent variable—the depth of floodwater above the first floor of a building. For flood loss analysis, two factors must be known—the floodwater elevation and the building FFE. FFE is an essential component in flood loss calculation, all too often building FFE information is unavailable due to the costly nature of elevation certificate preparation (FEMA, 2011b), for which a licensed surveyor is required, as well as the cost and effort required to maintain community building databases
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