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Residential building and sub-building level flood damage analysis using simple and complex models

Author

Listed:
  • Ryan Paulik

    (University of Auckland
    National Institute of Water and Atmospheric Research (NIWA))

  • Conrad Zorn

    (University of Auckland)

  • Liam Wotherspoon

    (University of Auckland)

Abstract

Flood damage assessment is critical for optimal risk management investments. Damage models evaluate physical damage or monetary loss from direct building exposure to flood hazard processes. Traditional models represent a simple relationship whereby physical damage increases with water depth. More complex models offer an improved understanding of vulnerability, analysing interactions between multiple hazard and exposure variables that drive damage. Our study investigates whether increasing model complexity and explanatory damage variables improves prediction precision and reliability at residential building and sub-building (component) levels. We evaluate simple and complex empirical univariable and multivariable models for flood damage prediction. The Random Forest algorithm learned on multiple hazard and exposure explanatory variables outperformed linear and non-linear univariable regression approaches. Random Forest model predictive precision was highest when learning was limited to water depth and several important explanatory damage variables (flow velocity, area and floor height). Component damage models demonstrated high predictive precision for internal finishes and services. Precision reduced for structure and external finishes as damage samples for model learning were limited. High performing but complex multivariable models require further spatio-temporal transfer investigation to determine opportunities for accurate and reliable object-specific flood damage prediction in data scarce locations.

Suggested Citation

  • Ryan Paulik & Conrad Zorn & Liam Wotherspoon, 2024. "Residential building and sub-building level flood damage analysis using simple and complex models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(14), pages 13493-13512, November.
  • Handle: RePEc:spr:nathaz:v:120:y:2024:i:14:d:10.1007_s11069-024-06756-1
    DOI: 10.1007/s11069-024-06756-1
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    References listed on IDEAS

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    1. H. Apel & G. Aronica & H. Kreibich & A. Thieken, 2009. "Flood risk analyses—how detailed do we need to be?," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 49(1), pages 79-98, April.
    2. Tina Gerl & Heidi Kreibich & Guillermo Franco & David Marechal & Kai Schröter, 2016. "A Review of Flood Loss Models as Basis for Harmonization and Benchmarking," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-22, July.
    3. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    4. Oliver E. J. Wing & Nicholas Pinter & Paul D. Bates & Carolyn Kousky, 2020. "New insights into US flood vulnerability revealed from flood insurance big data," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
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