Citation

Chow C, Andrášik R, Fischer B, Keiler M. J. Environ. Manage. 2019; 246: 85-100.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.jenvman.2019.05.084

PMID

unavailable

Abstract

Knowledge about the cause of differential structural damages following the occurrence of hazardous hydro-meteorological events can inform more effective risk management and spatial planning solutions. While studies have been previously conducted to describe relationships between physical vulnerability and features about building properties, the immediate environment and event intensity proxies, several key challenges remain. In particular, observations, especially those associated with high magnitude events, and studies designed to evaluate a comprehensive range of predictive features are both limited. To build upon previous developments, we described a workflow to support the continued development and assessment of empirical, multivariate physical vulnerability functions based on predictive accuracy. Within this workflow, we evaluated several statistical approaches, namely generalized linear models and their more complex alternatives. A series of models were built 1) to explicitly consider the effects of dimension reduction, 2) to evaluate the inclusion of interaction effects between and among predictors, 3) to evaluate an ensemble prediction method for applications where data observations are sparse, 4) to describe how model results can inform about the relative importance of predictors to explain variance in expected damages and 5) to assess the predictive accuracy of the models based on prescribed metrics. The utility of the workflow was demonstrated on data with characteristics of what is commonly acquired in ex-post field assessments. The workflow and recommendations from this study aim to provide guidance to researchers and practitioners in the natural hazards community.

Language: en

Keywords

Dimension reduction; Empirical physical vulnerability functions; Hydro-meteorological hazards; Multivariate analysis; Predictive accuracy; Proportional loss