Uncertainty Quantification of Flood Damage Estimation for Urban Drainage Risk Management

Masaru Morita, Yeou Koung Tung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The study presents an inundation damage estimation method which quantifies the uncertainty in inundation damage statistical data for urban drainage management. The flood damage is usually estimated by multiplying inundated asset value by damage rate determined by the inundation depth. The dispersions of the asset values and the damage rates related to the uncertainty were expressed quantitatively using probability distributions for the actual flood damage data surveyed by the national government. Monte Carlo simulation was utilized to calculate the damage from the two parameters with probability distributions. Thus the simulations brought about the monetary flood damage not in deterministic but in probabilistic form.

Original languageEnglish
Title of host publicationNew Trends in Urban Drainage Modelling - UDM 2018
EditorsGiorgio Mannina
PublisherSpringer Verlag
Pages470-474
Number of pages5
ISBN (Print)9783319998664
DOIs
Publication statusPublished - 2019 Jan 1
Event11th International Conference on Urban Drainage Modelling, UDM 2018 - Palermo, Italy
Duration: 2018 Sep 232018 Sep 26

Publication series

NameGreen Energy and Technology
ISSN (Print)1865-3529
ISSN (Electronic)1865-3537

Conference

Conference11th International Conference on Urban Drainage Modelling, UDM 2018
CountryItaly
CityPalermo
Period18/9/2318/9/26

Keywords

  • Flood damage estimation
  • Monte carlo simulation
  • Uncertainty

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Industrial and Manufacturing Engineering
  • Management, Monitoring, Policy and Law

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  • Cite this

    Morita, M., & Tung, Y. K. (2019). Uncertainty Quantification of Flood Damage Estimation for Urban Drainage Risk Management. In G. Mannina (Ed.), New Trends in Urban Drainage Modelling - UDM 2018 (pp. 470-474). (Green Energy and Technology). Springer Verlag. https://doi.org/10.1007/978-3-319-99867-1_80