Articles | Volume 17, issue 9
Research article
29 Sep 2017
Research article |  | 29 Sep 2017

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

Dennis Wagenaar, Jurjen de Jong, and Laurens M. Bouwer

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Cited articles

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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).
Final-revised paper