Articles | Volume 17, issue 9
Nat. Hazards Earth Syst. Sci., 17, 1683–1696, 2017
https://doi.org/10.5194/nhess-17-1683-2017

Special issue: Damage of natural hazards: assessment and mitigation

Nat. Hazards Earth Syst. Sci., 17, 1683–1696, 2017
https://doi.org/10.5194/nhess-17-1683-2017

Research article 29 Sep 2017

Research article | 29 Sep 2017

Multi-variable flood damage modelling with limited data using supervised learning approaches

Dennis Wagenaar et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by Editor and Referees) (22 Apr 2017) by Thomas Thaler
AR by Dennis Wagenaar on behalf of the Authors (02 Jun 2017)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (06 Jun 2017) by Thomas Thaler
RR by Sven Fuchs (08 Jun 2017)
RR by Anonymous Referee #1 (29 Jun 2017)
ED: Reconsider after major revisions (further review by Editor and Referees) (29 Jun 2017) by Thomas Thaler
AR by Dennis Wagenaar on behalf of the Authors (25 Jul 2017)  Author's response    Manuscript
ED: Publish as is (30 Jul 2017) by Thomas Thaler
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Short summary
Flood damage models are an important component of cost–benefit analyses for flood protection measures. Currently flood damage models predict the flood damage often only based on water depth. Recently, some progress has been made in also including other variables for this prediction. Data-intensive approaches (machine learning) have been applied to do this. In practice the required data for this are rare. We apply these new approaches on a new type of dataset (combination of different sources).
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